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date: 17 November 2017

Network Perspectives on Crime

Summary and Keywords

Though often not mentioned by name, the importance of social networks in explaining criminal behavior, delinquency, and patterns has long been recognized in the study of crime. Theories that explain criminal behavior at the individual level being learned through the impacts of peer influences presume that the transmission of ideas and influences flow among social ties (networks) that link individuals. Cultural theories of crime work in the same way. At the community level, delinquency and criminal behavior are born among members of a community or group that adhere to a particular cultural set of norms or beliefs. The concentration of crime in particular geographic areas results when there are insufficient ties among local residents to affect informal social control in the area. Impacted neighborhoods are often described as socially isolated, lacking social ties to institutions of power that provide the investment and services needed in a healthy community. The history of the formation and activities of street gangs is a clear example of how understanding the ties among individuals, and between groups of these individuals, matter in our understanding these phenomena. Comprehending social ties among gangs and gang members and employment of social network analysis (SNA) have become mainstays of local law enforcement efforts to address the issue of gang violence.

Much of the early criminological work that implicated social networks but did not explicitly acknowledge a network by name, or did not employ SNA on formal network data, did so because collecting such data is difficult at best and sometimes impossible. Though criminology has been a “late adopter” of SNA, the field is making great strides in this area. The National Longitudinal Study of Adolescent to Adult Health (Add Health) research program has provided a rich set of network data to explore issues of peer influence. Researchers are using carefully collected social network data at the individual and organizational level to better understand the ability of communities to self-regulate delinquency and crime in an area. Arrest data and field identification stops are being used to generate large networks in an effort to understand how one’s position in a larger social structure might be related to an actor’s involvement in future offending or victimization.

As the field of criminology continues to adopt a network perspective in the study of crime, it is important to understand the development of social networks within the field. Critically examining the strengths and weaknesses of network data, especially in terms of the process by which data are generated, can lead to better applications of network analysis in the future.

Keywords: social networks, SNA, social structure, social ties, peer effect, street gangs, criminal networks

Introduction

Social network analysis (SNA) is a research methodology that has become increasingly popular in recent years. The growth in the use of SNA is undoubtedly related to the realization, in the public sphere and academia alike, that we live in a small, connected world (Barabási, 2002; Watts, 2003). Other factors, such as the exponential growth in the availability of computing power (which is often critical to modeling techniques for network data) and specialized software (particularly open source software), have contributed to a rise of SNA in social science research, including for the study of crime (Papachristos, 2011). However, as Morselli (2009, p. 3) notes, the study of criminal networks “is indeed influenced by our times, but it is not simply fashionable.”

SNA is not simply a methodological tool. As Butts (2009, p. 414) puts it, “[t]o represent an empirical phenomenon as a network is a theoretical act.” In many ways, the theoretical underpinnings that have fueled the surge of research on crime and social networks have roots that go as far back as the earliest days of modern criminology. Whereas the explicit measurement and statistical modeling of social networks have taken awhile to take hold in criminology, the theoretical thinking that justifies their uses have been with us all along. In line with Morselli’s statement, the sudden interest in SNA in criminology should not be viewed as an outcome of researchers’ infatuation with a new toy that will wane as novelty wears off, but as a textbook example of a methodological and technological advance finally catching up with theoretical ideas. The “plan of attack” for this article follows the natural evolution of network perspectives of crime, from their theoretical beginnings to their most recent applications, using modern modeling techniques.

We first briefly introduce the intellectual history behind the study of social networks outside of criminology, and we outline some factors that make the study of both social networks and crime theoretically enriching and methodologically challenging. Second, we describe how the network perspective is consistent with a variety of criminological theories. Third, we outline some of the earliest formal applications of SNA to the study of crime. Finally, we discuss the growth of SNA for the study of crime and delinquency. This discussion is organized by thematic areas where important studies are described that have emphasized the direct measurement and modeling of social network data. The thematic areas include a) friendship networks and delinquency, b) co-offending networks, c) the efficiency-security tradeoff in criminal networks, d) street gang networks, e) violence and contagion, and f) neighborhoods, networks, and crime.

Social Networks and Crime

There is a long history of inquiry demonstrating that crime is an inherently social activity. Crime and delinquency are learned through affiliations with peers (e.g., Akers, 1998; Conway & McCord, 2002; Sutherland, 1939); crime is often committed in the company of others (e.g., Shaw & McKay, 1942; Warr, 2002), and it is facilitated or constrained by the organization of social relationships in communities, which in turn influences neighborhood social structure (e.g., Sampson & Groves, 1989). Individuals sometimes affiliate with groups organized around criminal endeavors in the form of gangs and other organized crime groups (e.g., Short & Strodtbeck, 1965). Scholars have granted a special importance to social networks in their theories for almost as long as they have studied crime.

Social network analysis is concerned with the study of interactions between units. These units can be people, groups, events or neighborhoods linked together by social relationships. Unlike regression analyses, where independence between observations is assumed, SNA is specifically designed to take into account the inter-dependencies between units of analyses. Network analysts consider a wide variety of relationships or ties between the units they study. These ties can be defined as different types of social relationships such as friendship or acquaintanceship, co-membership in a group, co-offending, or simultaneous attendance at an event, among others. It is beyond the scope of this article to cover exhaustively the intellectual foundations of SNA. That said, we encourage anyone interested in conducting SNA to take the time to understand the rich history surrounding the development of SNA as documented by Freeman (2004). Freeman traces the emergence of SNA to the earliest moments in social science history and chronicles the diffusion of the methodology across disciplinary boundaries. In summary, Freeman (2004) argues that the roots of the structural perspective so important to modern SNA began with the work of Auguste Comte—the founder of sociology—who posited that society should be regarded as a system of interconnected elements and that a scientific study of society should hinge on the examinations of patterns in social connections. Emile Durkheim, who was influenced by Comte, made implicit references to the importance of social ties in his description of mechanical and organic solidarity. These scholars recognized the importance of social structure for human behavior in society, and their ideas are intrinsically linked to modern SNA.

While it is impossible to ignore the importance of his intellectual forbearers, many recognize in George Simmel’s thinking about society the seeds of modern network perspectives in sociology. Simmel (1955) argued that society was made of multiple overlapping social circles and that our social world was constructed through our ties to a variety of social contexts and groups. The sociology of George Simmel provided social network analysis its theoretical backbone. In The Web of Group-Affiliation (1955), Simmel explained how one’s position at the intersection of different social groups constrains and shapes behavior and personality. In fact, Simmel argued that individuality is defined through the unique juxtaposition of these social circles: “the groups with which the individual is affiliated constitute a system of coordinates, as it were, such that each new group with which he becomes affiliated circumscribes him more exactly and more unambiguously” (Simmel, 1955, p. 140). Simmel’s approach to sociology essentially encapsulates the core assumption of modern SNA. One cannot reduce a society to a group of human beings; rather, such a group becomes a society once their members interact and influence one another. Therefore, Simmel (1971, p. 25) maintained, if “there is to be a science whose subject matter is society and nothing else, it must exclusively investigate these interactions, these kinds and forms of sociation.”

Although social networks lay at the center of criminological theories, network data can be challenging to collect, especially studies at a larger scale than the individual level (e.g., group, community, etc.). Thus, studies of crime and delinquency have often relied on indirect measures of networks simply out of necessity. This is especially true for studies examining peer influences on delinquency where, instead of measuring complete networks, the respondents are often asked to self-report on their peers’ involvement in delinquency (e.g., Warr & Stafford, 1991). Such an approach is problematic for two reasons. First, there is a well-established tendency for individuals to be poor evaluators of others’ behaviors. Second, while such an approach may inform researchers about the number of delinquent friends one has or the seriousness of delinquency within one’s friendship networks, it is silent on the structure of these networks. Based on many studies conducted in other disciplines, where the explicit measurement of social structure has had a lengthier history, it appears likely that the structure of social networks bears important implications for the study of criminal behavior (e.g., Haynie, 2001). Although criminologists have been slower than sociologists and psychologists to adopt SNA, recent studies have begun employing the methodology to explore the social structure of crime (Bouchard & Malm, 2016; Carrington, 2011; Gallupe, 2016; McGloin & Kirk, 2010; Papachristos, 2011).

Theoretical Relevance of Social Networks in Criminology

Social networks have held a central place in many criminological theories. Nevertheless, criminologists have been slower to adopt SNA compared to other social science disciplines (Papachristos, 2011). Increasingly though, students of crime have recognized the potential of SNA as a methodological tool, and also for our theoretical understanding of the social context of crime (e.g., McGloin & Nguyen, 2013; Morselli, 2009; Sarnecki, 2001; Weerman, 2003). For instance, Krohn (1986) suggested that network theory could enable criminologists to better define concepts that had been previously difficult to operationalize and had the potential to reconcile macro and micro-sociological theories. Generally, networks have been hypothesized to have two main functions in the etiology of criminal behaviors. First, social networks have been seen by theorists to serve as the conduits through which criminal behaviors are facilitated; crime is learned, criminal opportunities are accessed, and criminal attitudes are reinforced through social relationships. Second, social networks have been theorized to play a role in either facilitating or constraining criminal involvement at the community level; crime is likely to emerge in communities where the organization of informal social networks among local residents is weak, limiting the efficacy of community members to control the behavior of others. In the following section, some of the criminological theories that fall under the two broad mechanisms outlined in the previous section are reviewed.

Criminal Behavior and Social Transmission

For Edwin Sutherland—the father of modern criminology—“systematic criminal behavior is determined in a process of association with those who commit crimes, just as systematic lawful behavior is determined in a process of association with those who are law-abiding” (Sutherland, 1939, p. 4). Sutherland argued that the cause of criminal behavior was differential association—the extent to which the content of associations differs between criminal and law-abiding activity will influence the behavior of the person maintaining these associations. Criminal behavior involves skills and techniques that must be learned. It also involves codes of conduct, identification of criminal opportunities, and acceptance of certain values. While these aspects of criminal behavior need not to be learned through criminal affiliates, Sutherland (1939) argued that the frequency and consistency of criminal contacts would predict repeated involvement in criminal behavior.

An important part of Sutherland’s theory is that “personal characteristics or social situations cause crime only as they affect differential association or frequency and consistency of contacts with criminal patterns” (1939, p. 6). In other words, Sutherland places individual social networks at the center of explanations of criminal behavior. Personal characteristics—such as age, gender, intelligence, and personality—and social situations, such as poverty and education—do influence crime, but only insofar as these characteristics influence the composition of personal networks.

Sutherland never really specified the processes by which people learn criminal skills and attitudes from their peers, which has been a major critique of the theory. Social learning theory (Akers, 1998; Burgess & Akers, 1966) for instance, was designed to apply advances in psychology to explain how definitions favorable to crime are learned through peer associations. Akers (1998) argued that criminal definitions become internalized and affect behavior through the processes of reinforcement and imitation. Peers can be an important source for the initiation of delinquent behaviors through imitation, and its continuation through positive reinforcement associated with peer approval. Differential association theory has also lead scholars to consider the processes by which individuals acquire and use definitions favorable to crime. For instance, Sykes and Matza (1957) posit that delinquents develop a set of “techniques of neutralization” that enable them to rationalize engaging in criminal behavior. Sykes and Matza (1957) disagreed with the notion that delinquents may be fully emerged in a delinquent subculture. Rather they point out “the fact that the world of the delinquent is embedded in the larger world of those who conform cannot be overlooked” (Sykes & Matza, 1957, p. 666). To the authors, it is highly unlikely that a delinquent youth is strictly embedded in delinquent social circles, or that entire communities would subscribe to delinquent norms. Sykes and Matza (1957) argued that delinquents must reconcile law-breaking behavior with the demands of the dominant society and thus develop “justifications for deviance that are seen as valid by the delinquent but not by the legal system or society at large” (p. 666).

Social Networks and the Ecology of Crime

Social networks play an important role in macro-sociological explanations of crime. Social disorganization theory (Shaw & McKay, 1942) posits that crime rates are higher in communities that lack the ability to monitor and control the delinquent activities of their fellow residents. Shaw and McKay (1942) noticed that despite significant changes in their racial and ethnic composition, certain areas of Chicago consistently exhibited higher crime rates over the 30-year period they studied. These areas tended to be further from the city’s central business district and were neighborhoods with lower economic status. Given the stability of their findings despite important changes in the composition of these areas, Shaw and McKay (1942) pointed out that it was unlikely that differences in crime rates was related to the racial, ethnic, and immigrant composition of these neighborhood. Rather, they hypothesized that the same processes that led to socio-economic segregation in urban areas were responsible for delinquency rates. According to Park and Burgess’ (1925) theory of urban ecology, less desirable areas of the city were characterized by high population turnover and racial and ethnic heterogeneity, mostly because these neighborhoods were affordable to newcomers, but were soon abandoned by them as their economic situation improved. Shaw and McKay (1942) argued that such residential turnover weakened the informal systems of social control in a community, which ultimately led to delinquency.

Many scholars have sought to clarify some of Shaw and McKay’s insights by proposing new versions of social disorganization theory. Social networks hold an important place in contemporary treatments of social disorganization theory, though recent developments have led to different hypotheses related to the characteristics of community networks that are associated with lower crime rates, which can be grouped into the systemic model and the collective efficacy model.

The systemic model of social disorganization breaks down sources of social control into private, parochial, and formal levels (Bursik & Grasmick, 1993; Hunter,1985). The private level refers to intimate ties (e.g., friendship, family) between residents. Past research has shown that, in areas of high residential instability, such networks are difficult to establish (Bursik & Grasmick, 1993; Suttles, 1968). When intimate relationships between community residents are weak and superficial, they become ineffective conduits of social control because the threat of losing these relationships is not meaningful enough to deter behaviors that go against local norms and expectations (Bursik & Grasmick, 1993; Sampson & Groves, 1989). The parochial level refers to relationships of a less intimate nature than the private level, but ones that link residents through their co-participation in neighborhood activities and organizations (e.g., religious institutions, social clubs). The participation in these activities increases the frequency of interactions between residents and increases the likelihood of intervention when suspicious activities are observed in the neighborhood. Again, residential instability hinders the establishment and development of such networks, but some have argued that population heterogeneity also reduces effective social control of parochial networks. Bellair (1997) argued that heterogeneity might reduce the likelihood that residents will perceive communalities with their neighbors. Similarly, Kornhauser (1978) points out that heterogeneity may impede communication and reduce the ability of residents to agree on and work together towards achieving common goals. Finally, the formal level refers to networks that link community organizations to institutions and other networks outside the community. The absence of such networks reduces the effectiveness of local institutions to address problems within the neighborhood.

More recently, researchers have introduced the collective efficacy model of social disorganization theory. Sampson, Raudenbush, and Earls (1997, p. 918) define collective efficacy as “social cohesion among neighbors combined with their willingness to intervene on behalf of the common good.” Sampson et al. (1997) found that when 70% of the variation in collective efficacy could be explained by neighborhood disadvantage, immigrant concentration, and residential stability, collective efficacy was found to mediate the impact of these variables on neighborhood levels of violence. These results suggest that heterogeneity and inequalities at the neighborhood levels do not directly lead to crime and violence, but rather contribute to the erosion of social networks that foster the cohesion necessary to prevent crime. Strong and weak ties between residents are crucial because they are the conduits through which communities develop self-efficacy to resolve problems, monitor youths, and report suspicious behavior (Bellair, 1997; Sampson et al., 1997).

Early Formal Applications of Network Analysis to the Study of Crime

Moreno (1934) developed sociometry—the analysis of relationships between individuals in group settings—to study group processes in prison and in a school for delinquent girls. Despite these beginnings, it would take several decades before SNA would become a common tool used by scholars interested in crime. Still, a handful of studies of crime and delinquency have used sociometric analyses and later formal network analysis beginning in the 1960s.

Given the importance of social groups within the intellectual tradition that fostered SNA, it is without surprise that the earliest application of SNA to the study of crime was in street gang research. Klein and Crawford (1967; Klein, 1971) used sociometric analyses to examine the structure of street gangs. The authors were unsatisfied with approaches to the study of group cohesiveness in the context of juvenile gangs both for theoretical and methodological reasons. According to Klein and Crawford (1967), street gangs are unique in that gang cohesiveness is in large part defined by external pressures, rather than internal mechanisms, such as common group goals, and interpersonal attractions, which are typically held responsible for cohesiveness in other types of groups. The authors argued that gangs typically lack group goals (other than protection from other groups), have relatively unstable membership, have symbolic rather than clearly actualized and upheld group norms, and lack any kind of leadership that could rally members around organized actions. Even the use of a group name—a clear sign of collective identification to the group—varies widely among membership and is often established on the basis of an external criterion, such as a neighborhood or street name. Yet, Klein and Crawford (1967) show that gangs do exhibit cohesion; but according to them, gang cohesiveness is a function of external pressures exerted on the group, such as the common frustrations of members due to their socio-economic conditions of poverty and exclusion, but also—and importantly—to identification of external threats to the group from rival gangs and from the police, teachers, and other community actors reacting to the delinquency of the gang.

Klein and Crawford’s work is important not because of their arguments regarding the uniqueness of gangs compared to other groups. Many gang researchers before them had made similar arguments (e.g., Cohen, 1955; Sherif & Sherif, 1967; Short & Strodtbeck, 1965; Thrasher, 1927). What makes their study important is that they developed an explicit measure of cohesiveness based on member interactions using sociometric analysis. In fact, one of the measures they employ is mathematically identical to network density—a commonly used metric in SNA—which is a proportion of relationships observed in a network over the number of possible ties. They find that the most cohesive groups tend to be more delinquent. The authors also conduct an analysis of cliques—identifiable subgroups within a larger network—and find that gang members generally associate in small subgroups of similar age and gender. Klein and Crawford were the first to move from thick, qualitative descriptions of gang structure to the systematic collection of network data to empirically show the generally loose character of the gang social structure. These results were critical to Klein’s (1971) later demonstration of the unintended consequence of assigning street workers to juvenile gangs. Klein (1971) argued that assigning a street worker to a gang could increase cohesiveness, and therefore, increase the delinquency of members. These findings led to the abandonment of the use of street workers in the prevention of gang delinquency and serves to this day as an important cautionary tale for the design of effective gang control strategies and the importance of evaluating the outcome of these efforts (Gravel, Bouchard, Descormiers, Wong, & Morselli, 2013; Papachristos, 2013).

The work of Klein and Crawford (1967) and Klein (1971) have had a lasting impact on gang research and on criminology more broadly, thanks in large part to the theoretical insight brought forth by their sociometric analyses. Yet, network studies in criminology remained relatively rare in the 1970s and 1980s. Limitations in computational power is likely a factor that hindered the use of SNA in criminology, but perhaps a more important barrier was and remains the availability of data amenable to network analysis. Klein and Crawford (1967) relied on detached worker observations of gang member interactions that require substantial resources, and given that the use of such workers fell out of favor in the 1980s, it is not surprising that such data were hard to come by.

Meanwhile, in Sweden, Sarnecki (1986) began using official police records to construct youth co-offending networks. Police data have always been a staple of criminological research. The innovation behind Sarnecki’s method was that he used the same data many criminologists had used before and extracted new information from them: social networks. Sarnecki’s method was relatively simple: a relationship was assumed whenever any two individuals were either arrested or suspected of a crime with one another. Sarnecki (1986) employed a snowball sampling technique to construct the co-offending networks of juvenile offenders in “A-town.” The author first identified all juveniles (born after 1957) arrested for or suspected of a crime between 1975 and 1977. From this population of juveniles, the author then identified all individuals who had been arrested and/or suspected of committing a crime in the company of any member of this population of youths, including older individuals.

Sarnecki’s initial study, which he replicated in the 1990s in Stockholm (Sarnecki, 2001), led to several important conclusions about the group nature of juvenile delinquency. The author found that nearly half of the population of studied juveniles of “A-town” could be found within the co-offending network, and actors within this network accounted for 86% of all juvenile offenses known to the police (Sarnecki, 1986). The study also revealed that co-offending associations tended to be unstable and only rarely persisted over time. Nevertheless, Sarnecki (1986) observed that, despite the ephemeral nature of co-offending ties, certain groups of offenders persisted over time and formed much of the connectivity in the network. According to Sarnecki (1986, p. 129), juveniles joined the network through their association with actors of the network of “slightly greater age and delinquent experience,” suggesting that these co-offending ties may be paths through which delinquent norms and values are transmitted.

Given concerns related to the accuracy of police data to study delinquent networks, Sarnecki (1986) sought to test the validity of the sociometric data he extracted from records by conducting interviews with youths found in his network. Clearly, the examination of social networks that emerge from official records is likely to be only an approximation of the actual social networks of offenders. The author interviewed 29 youths who were part of a subgroup according to the police data. These youth were asked to name their three best friends, and to identify individuals knew from the police-based network. Collectively, these youths identified 57 best friends, 15 of whom were not known to the police during the study period, 35 belonged to the same subgroup identified in the network, and 7 could be found elsewhere in the network. When asked to identify individuals they knew from a list of 64 individuals associated with the same subgroups as the respondent, only one youth from the list was not identified by any of the respondents. Sarnecki’s analyses suggested that co-offending networks from police data did indeed reflect some of the most important relationships of the youth he studied. The use of official data to construct social network has been an important source of data, especially in recent years.

The Growing Influence of SNA in Criminology

This section covers several areas of criminology where formal SNA has been applied. With the recent growth in studies using SNA, it would be impossible to provide an extensive review of this research. Instead, a handful of studies are described, on several topics that have made important contributions and that illustrate the potential of taking a network approach to the study of crime and delinquency. Each section provides interested readers with a list of additional studies to consult.

Friendship Networks and Delinquency

The influence of delinquent peers is an important part of many criminological theories. Sutherland’s differential association theory has generated much empirical research to investigate the influence of delinquent peers on individual behavior, but until recently, these investigations had been limited to testing the influence of the number of delinquent peers, or peer level of involvement on one’s own behavior (e.g., Warr, 2002).

The National Longitudinal Study of Adolescent Health (often referred to as Add Health) allowed researchers to move beyond the counting of delinquent peers. Furthermore, the availability of network data was able to improve on past research that often relied on having individuals estimate the behaviors of their own peers and consider how the structure of peer networks influenced delinquency (Haynie, 2001; Haynie & Osgood, 2005). The Add Health survey includes data on a variety of behavioral indicators including delinquency, and importantly, on peer relations for a nationally representative sample of youths in grades 7 to 12 nested in schools. Moreover, the Add Health dataset includes social network data for entire schools, which were collected by asking every youth in a school to nominate up to five male and five female friends (Bearman, Moody, & Stovel, 1997). The Add Health survey is the first of its kind and has generated much research on the structure of adolescent friendship networks, including work on delinquent behavior.

Haynie’s (2001) study was one of the first to use Add Health data to examine the delinquency-peer relationship. An important contribution of this study was to confirm that the relationship between one’s own delinquency and that of her peers was not simply an artifact of inadequate measurement techniques. Researchers had raised concerns regarding the fact that measures of peer delinquency often relied on a subject’s own evaluation of her peers’ delinquent behaviors, arguing that individuals poorly estimate the behaviors of others, often assuming that their peers are more similar to them than they are in reality (e.g., Bauman & Fisher, 1986; Jussim & Osgood, 1989). A second major contribution of Haynie’s study was the finding that the structure of friendship networks moderates the delinquency-peer relationship. The author found that the positive relationship between delinquency and peer delinquency was stronger when adolescents occupy central locations in peer networks, have dense networks, and are nominated by many others (i.e., popular). Haynie (2001) particularly emphasized the importance of density in moderating the peer-delinquency association. Network density—the proportion of observed ties over all possible ties—is related to the notion of cohesion, and it appears that the delinquent influence of peers is much greater when one’s network is cohesive.

Other scholars using SNA have explored Haynie’s finding related to popularity and delinquency. Young (2014) used Add Health data to test the relationship between social status and delinquency hypothesized in Moffitt’s (1993) dual-taxonomy theory. The theory posits the existence of two groups of offenders: Life-course persistent individuals who offend throughout their lives and are characterized by several deficits that impact their behaviors, and Adolescent-limited delinquents who are temporary and situational offenders. Moffitt’s theory posits that delinquency becomes an adaptive response to a gap between youths’ attainment of physical maturity and their inability to benefit from the privileges of adult status. As Young (2014, p. 105) explains, delinquency becomes a signal for maturity, and “adolescent-limited individuals notice that life-course persistent youth engage in delinquency, therefore appearing “adult-like,” and mimic their behavior.” Young (2014) found evidence for a key mechanism of Moffitt’s theory, which suggests that life-course persistent individuals should see their popularity fluctuate in adolescence: as their peers come to view them as symbols of maturity, popularity should increase, and as their adolescent-limited peers transition into adult-roles, their popularity should decrease.

Popularity has also been studied in the context of more specific delinquent activities. For instance, Gallupe (2014) found a curvilinear relationship between sociometric popularity (i.e., being nominated as friends by many others) and alcohol use. Gallupe (2014) argued that this pattern was indicative of two pathways for adolescent alcohol use. Alcohol use was positively correlated to popularity for low to moderate users suggesting that alcohol use may be a mark of social status, while alcohol use was negatively associated with popularity for high alcohol users suggesting that heavy alcohol users were seen negatively by others. Furthermore, high alcohol users were found to exhibit more symptoms of depression highlighting the fact that alcohol use in this group may be more of a coping mechanism than a status-seeking activity. Kreager, Rulison, and Moody (2011) also identified a positive relationship between the social status of peer groups and their alcohol use. However, their study also suggests that the same relationship may not apply to other indicators of delinquency. Kreager et al. (2011) found that delinquent groups were less central (e.g., connected to many others) in school networks and less cohesive than non-delinquent groups.

A recurrent critique of research on peer influences on delinquency is that the relationship is actually due to a self-selection of individuals into groups prone to be involved in delinquency. Gottfredson and Hirschi (1990) argued that “adventuresome and reckless children who have difficulty making and keeping friends tend to end up with one another, creating groups made up of individuals who tend to self-control. The individuals in such groups will therefore tend to be delinquent, as will the group itself.”(p. 158). McGloin and Shermer (2009), also using Add Health data, set out to test Gottfredson and Hirschi’s claim that the association between peer delinquency and one’s own behavior was in fact a spurious relationship that better explained individual indicators of low self-control. The authors found that, although low self-control did have a significant impact on behavior, peer delinquency still remained a predictor of delinquent behavior. Interestingly, the authors found that self-control did influence network structure, suggesting that low self-control could influence involvement in delinquency through its direct influence on behavior, but also through its effect on the formation of friendships. However, Young (2011) directly tested the influence of self-control on friendship formation in Add Health data and found self-control to have a negligible influence.

Co-Offending Networks

Crime is often committed in the company of others (Reiss & Farrington, 1991; Warr, 2002). As Reiss and Farrington (1991) point out, co-offending differs substantially from association with delinquent peers, both theoretically and in terms of measurements. As we have seen in the previous section, researchers interested in associations with delinquent peers are generally measuring one’s exposure to delinquency more broadly in an attempt to identify social influences on delinquent behaviors. It is posited that co-offending is more precisely related to “the actual involvement of a person in illegal behavior with the same or different persons” (Reiss & Farrington, 1991, p. 361). Although most theoretical explanations of crime rarely delineate between the construct delinquent peers and co-offending, Sutherland’s differential association theory highlights two different patterns by which peers may influence delinquent behavior: “When criminal behavior is learned, the learning includes (a) techniques of committing the crime, which are sometimes very complicated, sometimes very simple; and (b) the specific direction of motives, drives, rationalizations, and attitudes” (Sutherland & Cressey, 1955, p. 78). While the latter mechanism is consistent with the more general influence of delinquent peers, the former directly refers to the learning of different skills or techniques necessary for the commission of a specific offense. Sutherland’s (1937) account of professional thieves directly illustrates how techniques and strategies of theft are often learned through mentorship and experiences acquired through co-offending.

The difference between delinquent peers and co-offenders is subtle but important: While most co-offenders may be delinquent peers, not all delinquent peers are co-offenders. Tremblay (1993) argues that, although delinquents tend to associate with other delinquents, it would be a mistake to assume that every delinquent in one’s network is an equally suitable choice of co-offender in a particular situation, or even that suitable co-offenders will be available when criminal opportunities arise. Tremblay (1993, p. 26) emphasizes that the search for a suitable co-offender may involve the balancing of two criteria: a) the identification of a co-offender with which one has the strongest possible tie as she is likely to be more trustworthy, and b) the identification of “weak but useful ties so as to increase the scope and value of crime opportunities.” Thus the search can be a somewhat paradoxical endeavor. On the one hand, criminal involvement requires considerations of trust that would be optimized by limiting co-offending relationships to strong ties. A co-offending relationship is a contract between at least two individuals, that they will not betray one another, and the success of such a contract often depends on the strength of the prior relationship between the individuals and the embeddedness within a larger set of relationships (e.g., Granovetter, 1985). The presence of strong ties in a network facilitates triadic closure: if A has a strong tie to B and to C, it is likely that B and C, at the very least, know one another (Granovetter, 1973) Closure is associated with increased trust because it generates many channels through which one can access information about another’s behavior (Burt, 2005). Thus, if someone has a reputation for being unreliable or if someone suddenly begins acting strangely, a closed network provides a sort of a warning system as the information can be accessed through multiple channels, not simply through one’s direct experience with the individual in question. This feature of closed networks is especially important in the context of secretive activities that involve substantial risk if discovered, such as in context of secret societies (Erickson, 1981), conspiracies (Baker & Faulkner, 1993), and terrorist organizations (Krebs, 2002).

On the other hand, individuals who share strong ties are likely to have access to the same information, and therefore, weak ties are necessary for some endeavors. While closure may enhance trust in networks, it also creates redundancy of information, thus limiting access to novel information. Access to the more lucrative and attractive criminal opportunities may necessitate access to co-offenders from more distant social circles. For instance, motor vehicle theft may not be a worthwhile endeavor if one does not have access to someone in a position to resell the stolen vehicle. Sales of stolen vehicles are often more complicated endeavors than sales of stolen smaller and cheaper items such as electronic goods, and those in position to do so may not be readily available in one’s close network (e.g., Sullivan, 1989; Tremblay, Talon, & Hurley, 2001). Much like job seekers in Granovetter’s (1974) classic study, offenders are likely to expand their access to criminal opportunities if they can make use of weak ties. Like strong ties, weak ties can be defined by the quality of the relationship between two individuals (e.g., acquaintances, co-workers), but they can also be identified by the structural features they create. Weak ties generally occur between actors whose respective social circles overlap very little, and as such they act as bridges between subgroups of actors in the network. Weak ties facilitate access to information that may not be available through strong ties, where information may be redundant. However, weak ties involve more risk as the reputation and trustworthiness of the other actor can only be verified through direct interactions.

At the dyadic level, co-offending relationships tend to exhibit features similar to friendship ties. Collaboration in crime tends to occur between individuals of the same age and gender (Carrington, 2011; Sarnecki, 2001; van Mastrigt & Carrington, 2014). Homophily—the tendency of individuals to associate with similar others (McPherson, Smith-Lovin, & Cook, 2001)—appears to play a similar role in co-offending networks as in many other types of social networks. Co-offending also tends to be more prevalent for juveniles and varies depending on the type of crime (Andresen & Felson, 2009; Carrington, 2009). Most research examining co-offending relationships at the dyadic level has relied on official police data, linking individuals through their participation in the same incidents. While insightful, such research does not take full advantage of the benefits of SNA.

Other studies have relied on official records and interviews to construct larger networks of offenders, which enable researchers to study different aspects of co-offending in context. Many researchers have pointed out the risk involved with the decision to co-offend (McCarthy, Hagan & Cohen, 1998; McGloin & Nguyen, 2012), and when it is not necessarily lucrative (Tillyer & Tillyer, 2015). On the other hand, others have shown that the structure of co-offending networks, as well as the context, may constrain or facilitate access to criminal opportunities. For instance, McGloin & Piquero (2010), using data on the co-offending networks of a sample of offenders, found that offenders with less redundant co-offending networks (i.e., less closure) engaged in a greater variety of crimes, whereas those with more redundant networks were more likely to be specialized when committing group crimes. Similarly, Morselli and Tremblay (2004) found that offenders with less redundant networks were more likely to report higher earnings from their participation in market offenses (e.g., drug trafficking, fencing), but not for predatory offenses (e.g., robbery, theft). Although they do not necessarily use formal social analysis, many studies have shown that the structure and composition of social networks of offenders are related to higher criminal earnings and cost-avoidance. For instance, Bouchard and Nguyen (2010) find that youths involved in cannabis cultivation who are embedded in large youth networks are more likely to be arrested compared to smaller youth networks. On the other hand, being a member of a network of adult growers significantly decreased the odds of arrest, especially being embedded in a large adult network. Furthermore, Bouchard and Ouellet (2011) showed that regardless of the size of a drug trafficking organization they were a part of, drug dealers were found to evade arrest for longer periods when their own personal network was larger.

Taken together, these results suggest that the structural position of individuals within co-offending networks does influence their access to criminal opportunities and how lucrative these are, as well as their likelihood of arrest. Most of the studies discussed in this section employ egocentric network data, that is, information about the relationship between a focal individual (ego) and others (alters) along with information about the relationships between alters. However, these ego networks are embedded in much larger structure. Studying the structure of such networks allows researchers to examine how criminal networks operate, and how they manage to balance the need for efficiency and security (Morselli, Giguère, & Petit, 2007).

The Efficiency-Security Tradeoff in Criminal Networks

High security in criminal networks often implies low efficiency, and vice-versa; thus, the structure of these networks will vary greatly depending on the objectives, the risk involved, and resources available (Morselli et al., 2007). Morselli et al. (2007) named this paradox the efficiency-security trade-off, based on the notion that actors in criminal networks are often faced with a balancing act about concerns regarding their illicit endeavors. According to the authors, the objectives and “time-to-task” are two of the characteristics that explain which side of the trade-off is emphasized by network participants. Ideologically driven actors, as in the case of terrorist networks, may prioritize security, while profit-driven criminal enterprises, such as in drug trafficking networks, may require greater efficiency. Morselli et al. (2007) argues that ideologically driven networks may be able to limit their visibility to authorities because participants are not expecting an immediate, tangible reward for their actions, and thus may be more willing to wait to spring into action. Conversely, since profit-driven network participants expect to be rewarded for their involvement, the network as a whole must be organized in such a way that facilitates ongoing actions. Given that criminal networks are most vulnerable when participants get together in the realization of the network’s objectives, enterprises that involve shorter time-to-task must sacrifice the security of their operations to enhance its efficiency.

Krebs’ (2002) analysis of the terrorist network involved in the September 11, 2001 attacks provides an example of the importance of security in ideologically driven networks. Krebs (2002) mapped the networks surrounding the 19 terrorists who hijacked planes, which included several co-conspirators who helped coordinate and fund the operation. The author observed that hijackers were connected in a snake-like network through prior trusted relationships—such as attending the same schools and living together—but overall the network was very sparse with very little connectivity between hijackers from distant parts of the network, even between hijackers who boarded the same plane. As Krebs (2002) notes, this is an important security feature of the network, as the detection of some individuals may not lead to the dismantlement of the entire operation. However, in order for the terrorist plot to be coordinated and executed, communication must flow between distant parts of the network. Krebs (2002, p. 46) shows that this was achieved through the “judicious use of transitory short-cut” in the network. For instance, some of the hijackers met in Las Vegas to discuss their plans. These short cuts are temporary ties that serve the purpose of coordination, but are only activated for a very short period and are only re-activated if needed. These temporary ties greatly reduce the social distance between participants, making the operation particularly vulnerable to detection and disruption by authorities when these connections are activated.

In their re-analysis of Krebs’ data, Morselli et al. (2007) note that most criminal network do not have the luxury of using these “transitory short-cuts” and substitute efficiency for security. They compared Krebs’ network to a drug-trafficking network and found that, while the absence of visible ties may increase the security in terrorist networks, the addition of participants in a drug trafficking network may also increase the security of the operation. Drug trafficking networks are often built outward from a core, and the addition of actors extends the periphery of the network, making it more difficult for the authorities to identify central players (Morselli, 2009; Morselli et al., 2007). Furthermore, peripheral actors may mask the illicit activities through their involvement in legitimate activities (e.g., legitimate importers, accountants, truckers, border agents).

Ouellet, Bouchard, and Hart (2017) showed how terrorist networks adapt when facing external threats to keep their activities concealed from the authorities. The authors observed that networks of Al-Qaeda terrorists exhibited less local closure post-9/11, a period when the terrorist organization was facing increased pressures from the War on Terror. Ouellet et al. (2017) argue that a reduction in local clustering contributed to the decentralization of operations. The authors point out that such a structural feature, although diminishing the efficiency of information flow between network participants, might increase the overall security of the operation by making it difficult for law enforcement to identify all moving parts of a planned operation. Ouellet et al. (2017) suggest that anti-terrorism efforts could potentially use the lack of local closure in these networks to their advantage. Without closure, internal mechanisms of monitoring are weak, and network participants may be less likely to become aware of the activities of their co-conspirators, which may open up opportunities for law enforcement to infiltrate the network, or at the very least disrupt activities by weakening trust between weakly connected terrorists.

Trust issues in criminal networks have also been observed in drug-trafficking networks. For instance, Malm, Bouchard, Decorte, Vlaemynck, and Wouters (2017) found that marijuana growers positioned in structural holes—that is, in positions that connect together areas of the networks that would otherwise be isolated—perceive their chances of getting caught to be much higher than those in more clustered areas of the network where ties between offenders are more redundant (i.e., there exist several paths through which two individuals may reach one another). The authors point out that individuals who occupy brokerage positions in these networks may have access to more information about police activities, which may make them more aware of their vulnerability. Additionally, growers in cohesive regions of the network may be more trusting of their co-offenders, since any suspicious activity within these redundant networks is likely to attract the attention of others. The study by Malm et al. (2017) highlights the potential SNA has for the study of deterrence. The authors observed that, contrary to general deterrence models, risk perceptions were not related to the punitiveness of laws applicable in the regions where growers operated or to the growers’ prior contacts with the police, but were varied primarily as a function of the structural positions of growers in the network.

Street Gang Networks

As noted previously, some of the earliest applications of SNA in criminology have been to the study of street gangs, and it remains an area where the methodology has been applied the most. Gang members are known to be more violent and delinquent than non-gang members (Pyrooz, Turanovic, Decker, & Wu, 2016), and many theorists have argued that an important mechanism to explain these differences lies in the structure and organization of these criminal groups (e.g., Short & Strodtbeck, 1965).

The activities gangs engage in also tend to depend on their relationships with other groups. For instance, gang violence is often reciprocal in that violence directed towards one gang is often met with retaliation. Furthermore, gangs are often part of a complex system of rivalries and alliances with other groups, and SNA has been used to examine these networks. Thus, SNA has obvious applications for the study of gangs.

Important innovations in the area of violence prevention—particularly gang violence—have further contributed to the growing use of SNA in criminology. Focused-deterrence policing strategies rely on the diffusion of deterrent messages to the most violent elements of a community. These strategies were first implemented in Boston under the name “Operation Ceasefire” (Braga, Kennedy, Waring, & Piehl, 2001) but have since then been replicated across the United States (e.g., Braga & Weisburd, 2015; Tita, Riley, Ridgeway, Grammich, & Abrahamse, 2003) and recently in the United Kingdom (Densley & Jones, 2016). Operation Ceasefire—and its replications—is built on the premise that if targeted adequately, law enforcement can take advantage of the social structure behind gang violence to convey to gang members involved in gun violence that such behavior will no longer be tolerated and special prosecution teams will seek every legal recourse available against those who persist (Kennedy, 2009). Unlike traditional deterrence-based approaches, where the threat of sanctions is either announced through broad media campaign or relies on the vicarious experience of punishment of would-be offenders, the success of focused-deterrence strategies hinges on the efficient targeting of the deterrent message to the most violent members of a community and its word-of-mouth diffusion through social networks (Kennedy, Piehl, & Braga, 1997). As such, SNA has played an important part in the implementation and evaluation of focused-deterrence approaches (Gravel & Tita, 2015).

The intuition behind programs like Operation Ceasefire, that street gang members’ networks could be co-opted to deliver deterrent messages, was quite innovative because it would take several years before scholars would apply SNA to study networks of gang violence. One notable application is Papachristos’ (2009) study of murder networks in Chicago. Papachristos constructed networks that map the patterns of homicide between street gangs in Chicago. He defined connections between two gangs where gang A attacked gang B, and created a directed network that connected multiple gangs. Papachristos (2009) found that gang violence in Chicago exhibited a remarkably stable structure, a fact that is particularly surprising given the well-documented lack of membership stability and relatively short period of individual involvement in gangs (Decker, 1996; Short & Strodtbeck, 1965; Thornberry, Krohn, Lizotte, Smith, & Tobin, 2003). Importantly, the author found that these networks become institutionalized over time and act as conduits through which gangs enter contests of social dominance. In other words, Papachristos (2009) showed that gang violence was perpetuated through social contagion and demonstrated that, although gangs may not be aware of the overall structure of the network, they do seem to take into account their position and status vis-a-vis other groups, and this structural position appears to have a significant impact on a given gang’s propensity to be involved in violence.

Since Papachristos’ (2009) “Murder by Structure,” several other studies have used SNA to study the structure of gang violence. Descormiers and Morselli (2011) mapped the networks of gang rivalries and alliances in Montreal. Unlike Papachristos (2009), who used police records of gang homicides to create his networks, Descormiers and Morselli (2011) conducted focus groups with gang-involved youth to establish which groups were enemies and which groups were allies. The authors’ main objective in collecting these data was to test whether the prevailing law enforcement view that inter-group interactions were organized under the Crips vs. Bloods dichotomy. While the authors found evidence of such a dichotomy, their analyses of the network of rivalries and alliances revealed a much more complex picture, with occasional alliance between consortium and rivalries within them as well. These findings suggest that, while there is some ground truth to the popular Bloods vs. Crips dichotomy, such a view is reductionist and hides more complex patterns of gang violence. Similar findings have been observed in Chicago, where network analyses revealed that smaller factions of larger gang “nations,“ such as the Gangster Disciples and Latin Kings, were found to be more meaningful units to target as part of a violence reduction strategy than the gang nations as a whole (Papachristos & Kirk, 2015).

Another important contribution to the study of gang violence was made possible through the combination of SNA and spatial analysis. Radil, Flint, and Tita (2010) recognized that, although it is true that much gang violence is the result of the territorial nature of street gangs, a more complete understanding of inter-gang patterns of violence must take into account the social networks that link gangs in communities with one another. Radil et al. (2010; see also Tita and Radil, 2011) found that, although it is true that gang violence tends to occur between geographically proximate groups, violence also tends to occur in social and geographical spaces between two rival gangs. In other words, while one would expect that most violence would occur between neighboring gangs, the addition of rivalry networks to the spatial analysis revealed that areas found between two connected gangs in the network (i.e., rival gangs) also saw higher levels of violence. Furthermore, gangs that were found to have their territories between two gangs connected by a rivalry in the social network were more likely to be attacked.

Street gangs have interested criminologists since the early beginnings of the discipline. Gang membership is a robust predictor of criminal involvement (e.g., Pyrooz et al., 2016), and it has long been argued that it is the social context of gangs that make them especially criminogenic (Short & Strodtbeck, 1965; Thrasher, 1927). As discussed, a critical contribution of SNA to the study of street gangs was made by Klein and Crawford’s (1967) and Klein’s (1971) confirmation that the internal structure of gang consisted of loosely connected cliques. This finding has allowed researchers to dispel the myth that gangs were well-organized, vertically-integrated groups. Several studies have followed this work and employed SNA to examine the internal structure of street gangs, but also, gang members’ connections to the broader criminal world. For instance, Papachristos (2006) reanalyzed the sociometric figures of the Erls—a Mexican gang described in Suttles’ (1968) The Social Order of the Slum, using a variety of network measures. The analysis replicates many of the qualitative findings from Suttle’s original analysis, such as the organization of the gang into cohesive subgroups loosely interconnecting, but it also highlights new features of the social organization of the gang. For example, while Papachristos (2006) finds one of the members considered to be the gang’s leader to be a central actor in the network, he also notes that many other members play important roles in the gang either through their position as mediators between members who are in conflict, or through their positions as bridges between the two cohesive subgroups.

As Morselli (2009) points out, many studies that have examined the social organization of street gangs tend to be limited to the study of activities that occur between gang members. The author argues that since gangs are far from being closed, cohesive groups, it is likely that gang networks will overlap with other gangs and include many non-gang actors. Morselli (2009) examined network data extracted from three different police investigations of a drug trafficking operation believed to be controlled by the Bo-Gars, a Montreal street gang. The author finds that although several Bo-Gars members—as well as members from other smaller gangs—could be found in the network, they did not occupy especially prominent positions. Moreover, these members rarely acted as a gang; they rarely communicated with one another, and they were far more likely to interact with non-gang members, suggesting that law enforcement’s belief that the gang controlled the operation was wrong. However, members of other smaller and less established gangs present in the network did tend to operate as small cliques. Morselli (2009) shows that the most important players in the drug trafficking network were non-gang members, with the Bo-Gars members being only indirectly connected to the most connected actors. That said, Morselli (2009) posits that the Bo-Gars members likely benefited from the more established reputation of their gang and were able to occupy positions that ensured their participation in the drug trafficking enterprise, while at the same time being removed enough from the operations to evade prosecution, a luxury that other members of less established gangs in the network did not have.

Morselli’s analysis showed that law enforcement’s focus on a gang they believed to control the drug trade was in fact misguided. In a similar vein, Bouchard and Konarski (2014) conducted an analysis of a co-offending network of gang members that had been targeted by law enforcement. The authors questioned the decision of officers to target six adolescents they believed to be the core members of the gang. Police identified these six individuals during past investigations, through witness statements, reviews of prior records, and self-identification by the members in question. Law enforcement officials believed that by targeting these core members, they would effectively dismantle the entire gang. Bouchard and Konarski (2014) recreated the co-offending network of the six targeted members and found that, while their network did exhibit a core group of individuals, with many others on the periphery, this core was significantly larger than the six targeted by law enforcement. In total, 13 individuals could be found within that core, including five out of the six targeted. Moreover, Bouchard and Konarski (2014) found that ten other actors were connected to more co-offenders than the six targeted.

Violence and Contagion

Social networks are responsible for the transmission of information, skills, and attitudes—concepts that are critical to criminological theories. Recently, SNA has been applied to investigate transmissions of another kind: violent victimization. Criminologists have often noted the overlap between offending and victimization—that is, the fact that today’s offenders tend to be tomorrow’s victim (see Jennings, Piquero, & Reingle, 2012 for a review). Hindelang, Gottfredson, and Garofalo (1978) proposed a theory in which they argued that victimization is a function of the extent to which the lifestyle of victims exposes them to offenders. The authors proposed that the odds of victimization increase as the routine activities of victims overlap with those of offenders. Hindelang et al. (1978, p. 255) also pointed out the importance of social relationships to explain victimization stating “social contacts and interactions occur disproportionately among individuals who share similar lifestyles.” The likelihood of victimization increases between individuals who are demographically similar because interactions between these individuals are far more likely. Recent work using SNA has enabled researchers to demonstrate the non-random and clustered nature of violence in social networks.

Papachristos, Braga, and Hureau (2012) used Field Intelligence Observation (FIO) cards—records of informal, non-criminal police-citizen encounters—to construct a social network of high-risk individuals in two Boston neighborhoods with high rates of violence. The authors found that a majority of gunshot victims (85%) could be found within a single network of 579 individuals. Furthermore, Papachristos et al. (2012) showed that individuals in the networks are on average 4.69 handshakes away from a gun victim, and the odds of a member of this network being victimized decreases by 25% for each degree of separation from a victim. A study conducted in Chicago found similar results: 41% of all homicide victims in a Chicago community were found within a co-offending network that included less than 4% of the community’s population, and each degree of separation from a victim decreased offending risk by 57% (Papachristos & Wildeman, 2014). When the study was replicated for the entire city of Chicago, Papachristos, Wildeman, and Roberto (2015) again found that 70% of all non-fatal shooting victims in the city could be found within a co-offending network that included only 6% of the population. Another study, this time conducted in Newark, New Jersey, found that social proximity to gang members also places individuals at-risk for gun violence victimization. Papachristos, Braga, Piza. and Grossman (2015) argued that social proximity to gang members, even if one is not a gang member herself, significantly increased the odds of being victimized.

Taken together, these results clearly show that violence is far from being randomly distributed. These results are by no means a surprise to criminologists, who have observed the clustering of violence within certain communities and among certain subsets of the population, but studies employing SNA have further narrowed down the identification of populations most at risk of violence. Furthermore, these studies make a compelling case that violence diffuses through communities through contagion-like processes (see Tracy, Braga, & Papachristos, 2016) and have significant implications for violence prevention policies, especially as it pertains to the ability of public officials to better target at-risk populations (Gravel & Tita, 2015). Importantly, as Papachristos et al. (2015, p. 148) put it, using SNA to target the population most at-risk for violence “would argue against sweeping policies and practices based purely on categorical distinctions such as race and ethnicity and, instead, opt for interventions and policies that consider the observable risky behavior of individuals.”

Neighborhoods, Networks, and Crime

The importance of networks in the development of social disorganization theory, collective efficacy, and most other ecological theories of crime has been discussed at length in the previous sections. However, several additional issues merit a brief discussion.

First, until recently, a close knit, socially cohesive community with a dense network of times among local residents was thought to be a sufficient condition resulting in a safe community. This presumption was first challenged by Mary Pattillo’s (1998, 2013) work on the duality of roles within a black middle class neighborhood, demonstrating that close friendship ties could inhibit the sharing of information with local authorities or otherwise thwart censuring of delinquency and crime. This notion was further developed by Browning, Feinberg, and Dietz (2004) with the formulation of the “negotiated coexistence model.” This model makes explicit that the ties among local residents can also facilitate opportunities for co-offending, the formation and sharing of norms that promote violence or delinquent behavior, and diminishing a community’s taste for involving formal agents of control and authority, namely the police.

Second, the geographic scale of social networks is being used to rethink the definition of a “neighborhood.” Ecological studies of crime have long wrestled with defining an appropriate spatial unit of analysis that encapsulates the social interactions and networks thought to regulate local patterns of crime. Until recently, most studies in the United States have relied on official census designations such as census tracts or block groups. Using large-scale social network data, Hipp and colleagues (Hipp & Boessen, 2013; Hipp, Faris, & Boessen, 2012) measure the spatial distribution of ties among adolescents to define the true extent of what they refer to as an “egohood.” The results from these studies offer promising confirmation of the importance of networks in terms of regulating crime and in terms of defining the true geographic footprint of a “neighborhood.”

Third, given the inherent difficulty of collecting network data at large scales, researchers have employed simulation models to estimate ties between community residents. Hipp, Butts, Acton, Nagle, and Boessen (2013) used the geographic location of households to estimate networks of interactions between residents based on spatial proximity. The authors find that areas that have a higher proportion of within area ties tend to have lower crime rates. Simulation methods such as those introduced by Hipp et al. (2013) are promising avenues to study the influence of community level ties on crime rates.

Finally, Bastomski, Brazil, and Papachristos (2017) used co-offending networks to examine the relationship between neighborhood level networks and rates of violence. The authors create inter-neighborhood networks based on the volume of co-offending that occurs between residents of different neighborhoods. Bastomski et al. (2017) find that homicide rates are related to the structural embeddedness of a neighborhood within the larger inter-neighborhood networks of co-offending. The closer neighborhoods are to the core of cohesive regions of the inter-neighborhood co-offending network, the higher homicide rates are for these neighborhoods.

Conclusion

Social networks have traditionally been given much lip service in discussion sections of research articles, but more recently have they begun to be explicitly measured and analyzed. Since Papachristos (2011, p. 101) observed that criminologists had “missed the boat” on the adoption of SNA compared to other social science disciplines, the use of SNA has seen an impressive growth in criminology—in quantity, but perhaps more importantly, in sophistication. Indeed, slowly (but surely), cross-sectional analyses are being supplemented with longitudinal network analyses; sophisticated analyses such as Exponential Random Graph Models (ERGMs) and Stochastic Actor-Oriented Models (SAOMs) are emerging where previous work relied on descriptive analyses. Researchers are showing how simple network concepts such as centrality, brokerage, and structural holes can be applied in the criminal context, but they are also finding creative applications to the study of crime of more complex network concepts and tools such as multiplexity, contagion, and community detection.

That said, while SNA holds much promise to improve our understanding of crime, it comes with a host of unique issues and challenges. In order for criminologists to truly take advantage of the network perspective, it is imperative that researchers not be blinded by the ever-increasing sophistication of modeling techniques, for the strength of SNA lies not in the wizardry of matrix algebra and graph theory, but in the unique interactional data on which the analysis is based. Anything can technically be a network; most things are not interesting networks. Butts’ (2009) characterization of network analysis as a “theoretical act” is both a statement about the potential of such analyses and a warning about the assumptions scholars make when defining what constitutes a node, a tie, and ultimately a network. A large proportion of the studies discussed have relied on secondary data from official sources, such as arrest records, court documents, media reports, and books. Although scholars have demonstrated their awareness of issues related to the validity and completeness of network data and have been cautious in interpreting their results, there is still much room for improvement in the critical examination of the quality of network data emerging from official data. How complete are these networks? Where is the boundary of a criminal network? How long is a tie active in a criminal network? What is the influence of well-known biases in official criminal justice data on network structures? Criminologists should know better than to use official records uncritically, given the long history of critical work on this issue.

Furthermore, social network analysts interested in crime should not become overly complacent and remain reliant on publicly available data sources such as Add Health, or relatively easily obtainable arrest data. The success of the network perspective is inherently tied to researchers’ ability and willingness to put in the hard work of collecting new data from diverse sources. That said, if the last few years are any indication of what is to come, the study of criminal network is likely to continue to mature and should inevitably confront these issues sooner rather than later. In sum, all the evidence points to the fact that Papachristos’ prediction in his 2011 piece “The Coming of Networked Criminology” came true: Networked criminology is here, and it is here to stay.

Further Reading

Friendship Networks and Delinquency

Gallupe, O., & Gravel, J. (2017). Social network position of gang members in schools: Implications for recruitment and gang prevention. Justice Quarterly, 34(3). Advance online publication.Find this resource:

Kreager, D. A., Young, J. T., Haynie, D. L., Bouchard, M., Schaefer, D. R., & Zajac, G. (2017). Where “old heads” prevail: Inmate hierarchy in a men’s prison unit. American Sociological Review, Advance online publication.Find this resource:

McGloin, J. M., Sullivan, C. J., & Thomas, K. J. (2014). Peer influence and context: The interdependence of friendship groups, schoolmates, and network density in predicting substance use. Journal of Youth and Adolescence, 43(9), 1436–1452.Find this resource:

Osgood, D. W., Ragan, D. T., Wallace, L., Gest, S. D., Feinberg, M. E., & Moody, J. (2013). Peers and the emergence of alcohol use: Influence and selection processes in adolescent friendship networks. Journal of Research on Adolescence, 23(3), 500–512.Find this resource:

Payne, D. C., & Cornwell, B. (2007). Reconsidering peer influences on delinquency: Do less proximate contacts matter? Journal of Quantitative Criminology, 23(2), 127–149.Find this resource:

Schaefer, D. R., Bouchard, M., Young, J. T., & Kreager, D. A. (2017). Friends in locked places: An investigation of prison inmate network structure. Social Networks. Advance online publication.Find this resource:

Schreck, C. J., Fisher, B. S., & Miller, J. M. (2004). The social context of violent victimization: A study of the delinquent peer effect. Justice Quarterly, 21(1), 23–47.Find this resource:

Weerman, F. M. (2011). Delinquent peers in context: A longitudinal network analysis of selection and influence effects. Criminology, 49(1), 253–286.Find this resource:

Weerman, F. M., Wilcox, P., & Sullivan, C. J. (2017). The short-term dynamics of peers and delinquent behavior: An analysis of bi-weekly changes within a high school student network. Journal of Quantitative Criminology. Advance online publication.Find this resource:

Young, J. T., Barnes, J. C., Meldrum, R. C., & Weerman, F. M. (2011). Assessing and explaining misperceptions of peer delinquency. Criminology, 49(2), 599–630.Find this resource:

Co-Offending Networks

Calderoni, F., Brunetto, D., & Piccardi, C. (2017). Communities in criminal networks: A case study. Social Networks, 48, 116–125.Find this resource:

Charette, Y., & Papachristos, A. V. (2017). The network dynamics of co-offending careers. Social Networks. Advance online publication.Find this resource:

Grund, T., & Morselli, C. (2017). Overlapping crime: Stability and specialization of co-offending relationships. Social Networks. Advance online publication.Find this resource:

Malm, A., Nash, R., & Vickovic, S. (2011). Co-offending networks in cannabis cultivation. In G. Potter, M. Bouchard, & T. Decorte (Eds.), World wide weed: Global trends in cannabis cultivation and its control (pp. 127–143). Burlington, VT: Ashgate.Find this resource:

McGloin, J., Sullivan, C. J., Piquero, A. R., & Bacon, S. (2008). Investigating the stability of co‐offending and co‐offenders among a sample of youthful offenders. Criminology, 46(1), 155–188.Find this resource:

Ouellet, F., Boivin, R., Leclerc, C., & Morselli, C. (2013). Friends with (out) benefits: Co-offending and re-arrest. Global Crime, 14(2–3), 141–154.Find this resource:

Schaefer, D. R. (2012). Youth co-offending networks: An investigation of social and spatial effects. Social Networks, 34(1), 141–149.Find this resource:

Schaefer, D. R., Rodriguez, N., & Decker, S. H. (2014). The role of neighborhood context in youth co‐offending. Criminology, 52(1), 117–139.Find this resource:

Smith, C. M., & Papachristos, A. V. (2016). Trust thy crooked neighbor: Multiplexity in Chicago organized crime networks. American Sociological Review, 81(4), 644–667.Find this resource:

The Efficiency-Security Tradeoff in Criminal Networks

Bouchard, M. (2015). Social networks, terrorism, and counter-terrorism: Radical and connected. New York: Routledge.Find this resource:

Bright, D. A., & Delaney, J. J. (2013). Evolution of a drug trafficking network: Mapping changes in network structure and function across time. Global Crime, 14(2–3), 238–260.Find this resource:

Bright, D. A., Hughes, C. E., & Chalmers, J. (2012). Illuminating dark networks: A social network analysis of an Australian drug trafficking syndicate. Crime, Law, and Social Change, 57(2), 151–176.Find this resource:

Calderoni, F. (2012). The structure of drug trafficking mafias: The ‘Ndrangheta and cocaine. Crime, law, and social change, 58(3), 321–349.Find this resource:

Malm, A., & Bichler, G. (2011). Networks of collaborating criminals: Assessing the structural vulnerability of drug markets. Journal of Research in Crime and Delinquency, 48(2), 271–297.Find this resource:

Morselli, C. (2001). Structuring Mr. Nice: Entrepreneurial opportunities and brokerage positioning in the cannabis trade. Crime, Law and Social Change, 35(3), 203–244.Find this resource:

Morselli, C., & Petit, K. (2007). Law-enforcement disruption of a drug importation network. Global Crime, 8(2), 109–130.Find this resource:

Natarajan, M. (2006). Understanding the structure of a large heroin distribution network: A quantitative analysis of qualitative data. Journal of Quantitative Criminology, 22(2), 171–192.Find this resource:

Reeves-Latour, M., & Morselli, C. (2016). Bid-rigging networks and state-corporate crime in the construction industry. Social Networks. Advance online publication.Find this resource:

Tenti, V., & Morselli, C. (2014). Group co-offending networks in Italy’s illegal drug trade. Crime, Law and Social Change, 62(1), 21–44.Find this resource:

Street Gangs

Baron, S. W., & Tindall, D. B. (1993). Network structure and delinquent attitudes within a juvenile gang. Social Networks, 15(3), 255–273.Find this resource:

Fleisher, M. S. (2005). Fieldwork research and social network analysis: Different methods creating complementary perspectives. Journal of Contemporary Criminal Justice, 21(2), 120–134.Find this resource:

Fleisher, M. S., & Krienert, J. L. (2004). Life‐course events, social networks, and the emergence of violence among female gang members. Journal of Community Psychology, 32(5), 607–622.Find this resource:

Grund, T. U., & Densley, J. A. (2015). Ethnic homophily and triad closure: Mapping internal gang structure using exponential random graph models. Journal of Contemporary Criminal Justice, 31(3), 354–370.Find this resource:

McCuish, E. C., Bouchard, M., & Corrado, R. R. (2015). The search for suitable homicide co-offenders among gang members. Journal of Contemporary Criminal Justice, 31(3), 319–336.Find this resource:

McGloin, J. M. (2007). The organizational structure of street gangs in Newark, New Jersey: A network analysis methodology. Journal of Gang Research, 15(1), 1.Find this resource:

Papachristos, A. V., Hureau, D. M., & Braga, A. A. (2013). The corner and the crew: The influence of geography and social networks on gang violence. American Sociological Review, 78(3), 417–447.Find this resource:

Pizarro, J. M., & McGloin, J. M. (2006). Explaining gang homicides in Newark, New Jersey: Collective behavior or social disorganization?. Journal of Criminal Justice, 34(2), 195–207.Find this resource:

Tremblay, P., Charest, M., Charette, Y., & Tremblay-Faulkner, M. (2016). Le délinquant affilié: La sous-culture des gangs de rue haïtiens de Montréal. Montreal: Liber. [The connected delinquent: Montreal’s Haitian street gang subculture]Find this resource:

Violence and Contagion

Bond, R. M., & Bushman, B. J. (2017). The contagious spread of violence among US adolescents through social networks. American Journal of Public Health, 107(2), 288–294.Find this resource:

Decker, S. H. (1996). Collective and normative features of gang violence. Justice Quarterly, 13(2), 243–264.Find this resource:

Green, B., Horel, T., & Papachristos, A. V. (2017). Modeling contagion through social networks to explain and predict gunshot violence in Chicago, 2006 to 2014. JAMA Internal Medicine, 177(3), 326–333.Find this resource:

Neighborhoods, Networks, and Crime

Bichler, G., Malm, A., & Enriquez, J. (2014). Magnetic facilities: Identifying the convergence settings of juvenile delinquents. Crime & Delinquency, 60(7), 971–998.Find this resource:

Boessen, A., Hipp, J. R., Butts, C. T., Nagle, N. N., & Smith, E. J. (2016). Social fabric and fear of crime: Considering spatial location and time of day. Social Networks. Advance online publication.Find this resource:

Browning, C. R. (2009). Illuminating the downside of social capital: Negotiated coexistence, property crime, and disorder in urban neighborhoods. American Behavioral Scientist, 52(11), 1556–1578.Find this resource:

Hipp, J. R. (2010). Micro-structure in micro-neighborhoods: A new social distance measure, and its effect on individual and aggregated perceptions of crime and disorder. Social Networks, 32(2), 148–159.Find this resource:

References

Akers, R. L. (1998). Social learning and social structure. Boston: Northeastern University Press.Find this resource:

Andresen, M. A. & Felson, M. (2009). The impact of co-offending. British Journal of Criminology, 50(1), 66–81.Find this resource:

Baker, W. E., & Faulkner, R. R. (1993). The social organization of conspiracy: Illegal networks in the heavy electrical equipment industry. American Sociological Review, 58(6), 837–860.Find this resource:

Barabási, A.-L. (2002). Linked: The new science of networks. New York: Perseus Book Group.Find this resource:

Baron, S. W., & Tindall, D. B. (1993). Network structure and delinquent attitudes within a juvenile gang. Social Networks, 15(3), 255–273.Find this resource:

Bastomski, S., Brazil, N., & Papachristos, A. V. (2017). Neighborhood co-offending networks, structural embeddedness, and violent crime in Chicago. Social Networks. Advance online publication.Find this resource:

Bauman, K. E., & Fisher, L. A. (1986). On the measurement of friend behavior in research on friend influence and selection: Findings from longitudinal studies of adolescent smoking and drinking. Journal of Youth and Adolescence, 15(4), 345–353.Find this resource:

Bearman, P. S., Moody, J., & Stovel, K. (1997). The Add Health network variable codebook. Chapel Hill: University of North Carolina.Find this resource:

Bellair, P. E. (1997). Social interaction and community crime: Examining the importance of neighbor networks. Criminology, 35(4), 677–704.Find this resource:

Bichler, G., Malm, A., & Enriquez, J. (2014). Magnetic facilities: Identifying the convergence settings of juvenile delinquents. Crime & Delinquency, 60(7), 971–998.Find this resource:

Boessen, A., Hipp, J. R., Butts, C. T., Nagle, N. N., & Smith, E. J. (2016). Social fabric and fear of crime: Considering spatial location and time of day. Social Networks. Advance online publication.

Bouchard, M. (2015). Social networks, terrorism, and counter-terrorism: Radical and connected. New York: Routledge.Find this resource:

Bouchard, M., & Konarski, R. (2014). Assessing the core membership of a youth gang from its co-offending network. In C. Morselli (Ed.), Crime and networks (pp. 81–93). New York: Routledge.Find this resource:

Bouchard, M., & Malm, A. (2016). Social network analysis and its contribution to research on crime and criminal justice. Oxford Handbooks Online.

Bouchard, M., & Nguyen, H. (2010). Is it who you know, or how many that counts? Criminal networks and cost avoidance in a sample of young offenders. Justice Quarterly, 27(1), 130–158.Find this resource:

Bouchard, M., & Ouellet, F. (2011). Is small beautiful? The link between risks and size in illegal drug markets. Global Crime, 12(1), 70–86.Find this resource:

Bond, R. M., & Bushman, B. J. (2017). The contagious spread of violence among US adolescents through social networks. American Journal of Public Health, 107(2), 288–294.Find this resource:

Braga, A. A., Kennedy, D. M., Waring, E. J., & Piehl, A. M. (2001). Problem-oriented policing, deterrence, and youth violence: An evaluation of Boston’s Operation Ceasefire. Journal of Research in Crime and Delinquency, 38(3), 195–225.Find this resource:

Braga, A. A., & Weisburd, D. L. (2015). Focused deterrence and the prevention of violent gun injuries: Practice, theoretical principles, and scientific evidence. Annual Review of Public Health, 36, 55–68.Find this resource:

Bright, D. A., & Delaney, J. J. (2013). Evolution of a drug trafficking network: Mapping changes in network structure and function across time. Global Crime, 14(2–3), 238–260.Find this resource:

Bright, D. A., Hughes, C. E., & Chalmers, J. (2012). Illuminating dark networks: A social network analysis of an Australian drug trafficking syndicate. Crime, Law and Social Change, 57(2), 151–176.Find this resource:

Browning, C. R. (2009). Illuminating the downside of social capital: Negotiated coexistence, property crime, and disorder in urban neighborhoods. American Behavioral Scientist, 52(11), 1556–1578.Find this resource:

Browning, C. R., Feinberg, S. L., & Dietz, R. D. (2004). The paradox of social organization: Networks, collective efficacy, and violent crime in urban neighborhoods. Social Forces, 83(2), 503–534.Find this resource:

Burgess, R. L., & Akers, R. L. (1966). A differential association-reinforcement theory of criminal behavior. Social Problems, 14(2), 128–147.Find this resource:

Bursik, R., & Grasmick, H. G. (1993). Neighborhoods and crime: The dimensions of effective community control. Lexington, KY: Lexington Books.Find this resource:

Burt, R. S. (2005). Brokerage and closure: Introduction to social capital. New York: Oxford University Press.Find this resource:

Butts, C. T. (2009). Revisiting the foundations of network analysis. Science, 325(5939), 414–416.Find this resource:

Calderoni, F. (2012). The structure of drug trafficking mafias: The ‘Ndrangheta and cocaine. Crime, Law and Social Change, 58(3), 321–349.Find this resource:

Calderoni, F., Brunetto, D., & Piccardi, C. (2017). Communities in criminal networks: A case study. Social Networks, 48, 116–125.Find this resource:

Carrington, P. J. (2009). Co‐offending and the development of the delinquent career. Criminology, 47(4), 1295–1329.Find this resource:

Carrington, P. J. (2011). Crime and social network analysis. In J. Scott & P. J. Carrington (Eds.), The SAGE handbook of social network analysis (pp. 236–255). Thousand Oaks, CA: SAGE.Find this resource:

Charette, Y., & Papachristos, A. V. (2017). The network dynamics of co-offending careers. Social Networks, Advance online publication.Find this resource:

Cohen, A. K. (1955). Delinquent boys: The culture of the gang. New York: Free Press.Find this resource:

Conway, K. P., & McCord, J. (2002). A longitudinal examination of the relation between co‐offending with violent accomplices and violent crime. Aggressive Behavior, 28(2), 97–108.Find this resource:

Decker, S. H. (1996). Collective and normative features of gang violence. Justice Quarterly, 13(2), 243–264.Find this resource:

Densley, J. A., & Jones, D. S. (2016). Pulling levers on gang violence in London and St. Paul. In C. L. Maxson, and F.-A. Esbensen (Eds.), Gang transitions and transformations in an international context (pp. 291–305). New York: Springer.Find this resource:

Descormiers, K., & Morselli, C. (2011). Alliances, conflicts, and contradictions in Montreal’s street gang landscape. International Criminal Justice Review, 21(3), 297–314.Find this resource:

Erickson, B. H. (1981). Secret societies and social structure. Social Forces, 60(1), 188–210.Find this resource:

Fleisher, M. S. (2005). Fieldwork research and social network analysis: Different methods creating complementary perspectives. Journal of Contemporary Criminal Justice, 21(2), 120–134.Find this resource:

Fleisher, M. S., & Krienert, J. L. (2004). Life‐course events, social networks, and the emergence of violence among female gang members. Journal of Community Psychology, 32(5), 607–622.Find this resource:

Freeman, L. C. (2004). The development of social network analysis: A study in the sociology of science. Vancouver, BC: Empirical Press.Find this resource:

Gallupe, O. (2014). Social status versus coping as motivation for alcohol use. Journal of Youth Studies, 17(1), 79–91.Find this resource:

Gallupe, O. (2016). Network analysis. In B. M. Huebner & T. S. Bynum (Eds.), The handbook of measurement issues in criminology and criminal justice (pp. 555–575). Oxford: John Wiley.Find this resource:

Gallupe, O., & Gravel, J. (2017). Social network position of gang members in schools: Implications for recruitment and gang prevention. Justice Quarterly. Advance online publication.Find this resource:

Gottfredson, M. R.; & Hirschi, T. (1990). A general theory of crime. Stanford, CA: Stanford University Press.Find this resource:

Gravel, J., Bouchard, M., Descormiers, K., Wong, J. S., & Morselli, C. (2013). Keeping promises: A systematic review and a new classification of gang control strategies. Journal of Criminal Justice, 41(4), 228–242.Find this resource:

Gravel, J., & Tita, G. E. (2015). With great methods come great responsibilities: Social network analysis in the implementation and evaluation of gang programs. Criminology & Public Policy, 14(3), 559–572.Find this resource:

Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360–1380.Find this resource:

Granovetter, M. S. (1974). Getting a job: A study of contacts and careers. Chicago: University of Chicago Press.Find this resource:

Granovetter, M. S. (1985). Economic action and social structure: The problem of embeddedness. American Journal of Sociology, 91(3), 481–510.Find this resource:

Green, B., Horel, T., & Papachristos, A. V. (2017). Modeling contagion through social networks to explain and predict gunshot violence in Chicago, 2006 to 2014. JAMA Internal Medicine. Advance online publication.Find this resource:

Grund, T. U., & Densley, J. A. (2015). Ethnic homophily and triad closure: Mapping internal gang structure using exponential random graph models. Journal of Contemporary Criminal Justice, 31(3), 354–370.Find this resource:

Grund, T. U., & Morselli, C. (2017). Overlapping crime: Stability and specialization of co-offending relationships. Social Networks. Advance online publication.Find this resource:

Haynie, D. L. (2001). Delinquent peers revisited: Does network structure matter? American Journal of Sociology, 106(4), 1013–1057.Find this resource:

Haynie, D. L., & Osgood, D. W. (2005). Reconsidering peers and delinquency: How do peers matter? Social Forces, 84(2), 1109–1130.Find this resource:

Hindelang, M. J., Gottfredson, M. R., & Garofalo, J. (1978). Victims of personal crime: An empirical foundation for a theory of personal victimization. Cambridge, MA: Ballinger.Find this resource:

Hipp, J. R. (2010). Micro-structure in micro-neighborhoods: A new social distance measure, and its effect on individual and aggregated perceptions of crime and disorder. Social Networks, 32(2), 148–159.Find this resource:

Hipp, J. R., & Boessen, A. (2013). Egohoods as waves washing across the city: A new measure of “neighborhoods.” Criminology, 51(2), 287–327.Find this resource:

Hipp, J. R., Butts, C. T., Acton, R., Nagle, N. N., & Boessen, A. (2013). Extrapolative simulation of neighborhood networks based on population spatial distribution: Do they predict crime? Social Networks, 35(4), 614–625.Find this resource:

Hipp, J. R., Faris, R. W., & Boessen, A. (2012). Measuring “neighborhood”: Constructing network neighborhoods. Social Networks, 34(1), 128–140.Find this resource:

Hunter, A. (1985). Private, parochial, and public social orders: The problem of crime and incivility in urban communities. In G. Suttles & M. Zald (Eds.), The challenge of social control: Citizenship and institution building in modern society (pp. 230–242). Norwood, NJ: Ablex.Find this resource:

Jennings, W. G., Piquero, A. R., & Reingle, J. M. (2012). On the overlap between victimization and offending: A review of the literature. Aggression and Violent Behavior, 17(1), 16–26.Find this resource:

Jussim, L., & Osgood, D. W. (1989). Influence and similarity among friends: An integrative model applied to incarcerated adolescents. Social Psychology Quarterly, 52(2), 98–112.Find this resource:

Kennedy, D. M. (2009). Deterrence and crime prevention: Reconsidering the prospect of sanction. New York: Routledge.Find this resource:

Kennedy, D. M., Braga, A. A., & Piehl, A. M. (1997). The (un)known universe: Mapping gangs and gang violence in Boston. In D. Weisburd & T. McEwen (Eds.), Crime mapping and crime prevention (pp. 219–262). Monsey, NY: Willow Tree Press.Find this resource:

Klein, M. W. (1971). Street gangs and street workers. Englewood Cliffs, NJ: Prentice-Hall.Find this resource:

Klein, M. W., & Crawford, L. Y. (1967). Groups, gangs, and cohesiveness. Journal of Research in Crime and Delinquency, 4(1), 63–75.Find this resource:

Kornhauser, R. R. (1978). Social sources of delinquency: An appraisal of analytic models. Chicago: University of Chicago Press.Find this resource:

Kreager, D. A., Rulison, K., & Moody, J. (2011). Delinquency and the structure of adolescent peer groups. Criminology, 49(1), 95–127.Find this resource:

Kreager, D. A., Young, J. T., Haynie, D. L., Bouchard, M., Schaefer, D. R., & Zajac, G. (2017). Where “old heads” prevail: Inmate hierarchy in a men’s prison unit. American Sociological Review, Advance online publication.Find this resource:

Krebs, V. E. (2002). Mapping networks of terrorist cells. Connections, 24(3), 43–52.Find this resource:

Krohn, M. D. (1986). The web of conformity: A network approach to the explanation of delinquent behavior. Social Problems, 33(6), 81–93.Find this resource:

Malm, A., & Bichler, G. (2011). Networks of collaborating criminals: Assessing the structural vulnerability of drug markets. Journal of Research in Crime and Delinquency, 48(2), 271–297.Find this resource:

Malm, A., Bouchard, M., Decorte, T., Vlaemynck, M., & Wouters, M. (2017). More structural holes, more risk? Network structure and risk perception among marijuana growers. Social Networks. Advance online publication.

Malm, A., Nash, R., & Vickovic, S. (2011). Co-offending networks in cannabis cultivation. In G. Potter, M. Bouchard, & T. Decorte (Eds.), World wide weed: Global trends in cannabis cultivation and its control (pp. 127–143). Burlington, VT: Ashgate.Find this resource:

McCarthy, B., Hagan, J., & Cohen, L. E. (1998). Uncertainty, cooperation, and crime: Understanding the decision to co-offend. Social Forces, 77(1), 155–184.Find this resource:

McCuish, E. C., Bouchard, M., & Corrado, R. R. (2015). The search for suitable homicide co-offenders among gang members. Journal of Contemporary Criminal Justice, 31(3), 319–336.Find this resource:

McGloin, J. M. (2007). The organizational structure of street gangs in Newark, New Jersey: A network analysis methodology. Journal of Gang Research, 15(1), 1.Find this resource:

McGloin, J. M., & Kirk, D. S. (2010). An overview of social network analysis. Journal of Criminal Justice Education, 21(2), 169–181.Find this resource:

McGloin, J. M., & Nguyen, H. (2012). It was my idea: Considering the instigation of co‐offending. Criminology, 50(2), 463–494.Find this resource:

McGloin, J. M., & Nguyen, H. (2013). The importance of studying co-offending networks for criminological theory and policy. In C. Morselli (Ed.), Crime and networks (pp. 13–27). New York: Routledge.Find this resource:

McGloin, J. M., & Piquero, A. R. (2010). On the relationship between co-offending network redundancy and offending versatility. Journal of Research in Crime and Delinquency, 47(1), 63–90.Find this resource:

McGloin, J. M., & Shermer, L. O. (2009). Self-control and deviant peer network structure. Journal of Research in Crime and Delinquency, 46(1), 35–72.Find this resource:

McGloin, J. M., Sullivan, C. J., Piquero, A. R., & Bacon, S. (2008). Investigating the stability of co‐offending and co‐offenders among a sample of youthful offenders. Criminology, 46(1), 155–188.Find this resource:

McGloin, J. M., Sullivan, C. J., & Thomas, K. J. (2014). Peer influence and context: The interdependence of friendship groups, schoolmates, and network density in predicting substance use. Journal of Youth and Adolescence, 43(9), 1436–1452.Find this resource:

McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27(1), 415–444.Find this resource:

Moffitt, T. E. (1993). Adolescence-limited and life-course-persistent antisocial behavior: A developmental taxonomy. Psychological Review, 100(4), 674–701.Find this resource:

Moreno, J. L. (1934). Who shall survive? A new approach to the problem of human interrelations. Washington, DC: Nervous and Mental Disease Publishing.Find this resource:

Morselli, C. (2001). Structuring Mr. Nice: Entrepreneurial opportunities and brokerage positioning in the cannabis trade. Crime, Law, and Social Change, 35(3), 203–244.Find this resource:

Morselli, C. (2009). Inside criminal networks. New York: Springer.Find this resource:

Morselli, C., Giguère, C., & Petit, K. (2007). The efficiency/security trade-off in criminal networks. Social Networks, 29(1), 143–153.Find this resource:

Morselli, C., & Petit, K. (2007). Law-enforcement disruption of a drug importation network. Global Crime, 8(2), 109–130.Find this resource:

Morselli, C., & Tremblay, P. (2004). Criminal achievement, offender networks, and the benefits of low self‐control. Criminology, 42(3), 773–804.Find this resource:

Natarajan, M. (2006). Understanding the structure of a large heroin distribution network: A quantitative analysis of qualitative data. Journal of Quantitative Criminology, 22(2), 171–192.Find this resource:

Ouellet, F., Boivin, R., Leclerc, C., & Morselli, C. (2013). Friends with(out) benefits: co-offending and re-arrest. Global Crime, 14(2–3), 141–154.Find this resource:

Ouellet, M., Bouchard, M., & Hart, M. (2017). Criminal collaboration and risk: The drivers of Al Qaeda’s network structure before and after 9/11. Social Networks. Advance online publication.Find this resource:

Osgood, D. W., Ragan, D. T., Wallace, L., Gest, S. D., Feinberg, M. E., & Moody, J. (2013). Peers and the emergence of alcohol use: Influence and selection processes in adolescent friendship networks. Journal of Research on Adolescence, 23(3), 500–512.Find this resource:

Papachristos, A. V. (2006). Social network analysis and gang research: Theory and methods. In J. F. Short & L. A. Hughes (Eds.), Studying youth gangs (pp. 99–116). Lanham, MD: Altamira.Find this resource:

Papachristos, A. V. (2009). Murder by structure: Dominance relations and the social structure of gang homicide. American Journal of Sociology, 115(1), 74–128.Find this resource:

Papachristos, A. V. (2011). The coming of a networked criminology. In J. MacDonald (Ed.), Advances in criminological theory (pp. 101–140), New Brunswick, NJ: Transaction Publishers.Find this resource:

Papachristos, A. V. (2013). The importance of cohesion for gang research, policy, and practice. Criminology & Public Policy, 12(1), 49–58.Find this resource:

Papachristos, A. V., Braga, A. A., & Hureau, D. M. (2012). Social networks and the risk of gunshot injury. Journal of Urban Health, 89(6), 992–1003.Find this resource:

Papachristos, A. V., Braga, A. A., Piza, E., & Grossman, L. S. (2015). The company you keep? The spillover effects of gang membership on individual gunshot victimization in a co‐offending network. Criminology, 53(4), 624–649.Find this resource:

Papachristos, A. V., & Kirk, D. S. (2015). Changing the street dynamic: Evaluating Chicago’s Group Violence Reduction Strategy. Criminology, & Public Policy, 14(3), 525–558.Find this resource:

Papachristos, A. V., Hureau, D. M., & Braga, A. A. (2013). The corner and the crew: The influence of geography and social networks on gang violence. American Sociological Review, 78(3), 417–447.Find this resource:

Papachristos, A. V., & Wildeman, C. (2014). Network exposure and homicide victimization in an African American community. American Journal of Public Health, 104(1), 143–150.Find this resource:

Papachristos, A. V., Wildeman, C., & Roberto, E. (2015). Tragic, but not random: The social contagion of nonfatal gunshot injuries. Social Science & Medicine, 125, 139–150.Find this resource:

Park, R. E., & Burgess, E. W. (1925). The city. Chicago: University of Chicago Press.Find this resource:

Pattillo, M. E. (1998). Sweet mothers and gangbangers: Managing crime in a black middle-class neighborhood. Social Forces, 76(3): 747–774.Find this resource:

Pattillo, M. E. (2013). Black picket fences: Privilege and peril among the black middle class. Chicago: University of Chicago Press.Find this resource:

Payne, D. C., & Cornwell, B. (2007). Reconsidering peer influences on delinquency: Do less proximate contacts matter? Journal of Quantitative Criminology, 23(2), 127–149.Find this resource:

Pizarro, J. M., & McGloin, J. M. (2006). Explaining gang homicides in Newark, New Jersey: Collective behavior or social disorganization? Journal of Criminal Justice, 34(2), 195–207.Find this resource:

Pyrooz, D. C., Turanovic, J. J., Decker, S. H., & Wu, J. (2016). Taking stock of the relationship between gang membership and offending: A meta-analysis. Criminal Justice and Behavior, 43(3), 365–397.Find this resource:

Radil, S. M., Flint, C., & Tita, G. E. (2010). Spatializing social networks: Using social network analysis to investigate geographies of gang rivalry, territoriality, and violence in Los Angeles. Annals of the Association of American Geographers, 100(2), 307–326.Find this resource:

Reeves-Latour, M., & Morselli, C. (2016). Bid-rigging networks and state-corporate crime in the construction industry. Social Networks. Advance online publication.Find this resource:

Reiss, A. J., & Farrington, D. P. (1991). Advancing knowledge about co-offending: Results from a prospective longitudinal survey of London males. Journal of Criminal Law and Criminology, 82(2), 360–395.Find this resource:

Sarnecki, J. (1986). Delinquent networks. Stockholm, Sweden: National Council for Crime Prevention Sweden.Find this resource:

Sarnecki, J. (1990). Delinquent networks in Sweden. Journal of Quantitative Criminology, 6(1), 31–50.Find this resource:

Sarnecki, J. (2001). Delinquent networks: Youth co-offending in Stockholm. Cambridge, U.K.: Cambridge University Press.Find this resource:

Sampson, R. J., & Groves, W. B. (1989). Community structure and crime: Testing social-disorganization theory. American Journal of Sociology, 94(4), 774–802.Find this resource:

Sampson, R. J., Raudenbush, S. W., & Earls, F. (1997). Neighborhoods and violent crime: A multilevel study of collective efficacy. Science, 277(5328), 918–924.Find this resource:

Schaefer, D. R. (2012). Youth co-offending networks: An investigation of social and spatial effects. Social networks, 34(1), 141–149.Find this resource:

Schaefer, D. R., Bouchard, M., Young, J. T., & Kreager, D. A. (2017). Friends in locked places: An investigation of prison inmate network structure. Social Networks. Advance online publication.Find this resource:

Schaefer, D. R., Rodriguez, N., & Decker, S. H. (2014). The role of neighborhood context in youth co‐offending. Criminology, 52(1), 117–139.Find this resource:

Schreck, C. J., Fisher, B. S., & Miller, J. M. (2004). The social context of violent victimization: A study of the delinquent peer effect. Justice Quarterly, 21(1), 23–47.Find this resource:

Shaw, C. R., & McKay, H. D. (1942). Juvenile delinquency and urban areas: A study of rates of delinquents in relation to differential characteristics of local communities in American cities. Chicago: University of Chicago Press.Find this resource:

Sherif, M., & Sherif, C. W. (1967). Group processes and collective interaction in delinquent activities. Journal of Research in Crime and Delinquency, 4(1), 43–62.Find this resource:

Short, J. F., & Strodtbeck, F. L. (1965). Group process and gang delinquency. Chicago: University of Chicago Press.Find this resource:

Simmel, G. (1971). The problem of sociology. In D. N. Levine (Ed.), Georg Simmel: On individuality and social forms. Chicago: University of Chicago Press.Find this resource:

Simmel, G. (1955). The web of group-affiliation. New York: Free Press.Find this resource:

Sutherland, E. H. (1937). The professional thief. Chicago: University of Chicago Press.Find this resource:

Smith, C. M., & Papachristos, A. V. (2016). Trust thy crooked neighbor: Multiplexity in Chicago organized crime networks. American Sociological Review, 81(4), 644–667.Find this resource:

Sutherland, E. H. (1939). Principles of criminology. Philadelphia, PA: JB Lippincott.Find this resource:

Sutherland, E. H., & Cressey, D. R. (1955). Principles of criminology (5th ed.). Philadelphia, PA: J. B. Lippincott.Find this resource:

Suttles, G. D. (1968). The social order of the slum: Ethnicity and territory in the inner city. Chicago: University of Chicago Press.Find this resource:

Sullivan, M. L. (1989). “Getting paid”: Youth crime and work in the inner city. Ithaca, NY: Cornell University Press.Find this resource:

Sykes, G. M., & Matza, D. (1957). Techniques of neutralization: A theory of delinquency. American Sociological Review, 22(6), 664–670.Find this resource:

Tenti, V., & Morselli, C. (2014). Group co-offending networks in Italy’s illegal drug trade. Crime, Law, and Social Change, 62(1), 21–44.Find this resource:

Thornberry, T. P., Krohn, M. D., Lizotte, A J., Smith, C. A., & Tobin, K. (2003). Gangs and delinquency in developmental perspective. New York: Cambridge University Press.Find this resource:

Thrasher, F. M. (1927). The gang: A study of 1,313 gangs in Chicago. Chicago: University of Chicago Press.Find this resource:

Tillyer, M. S., & Tillyer, R. (2015). Maybe I should do this alone: A comparison of solo and co-offending robbery outcomes. Justice Quarterly, 32(6), 1064–1088.Find this resource:

Tita, G., Riley, K. J., Ridgeway, G., Grammich, C. A., & Abrahamse, A. (2003). Reducing gun violence: Results from an intervention in East Los Angeles. Santa Monica, CA: RAND.Find this resource:

Tita, G. E., & Radil, S. M. (2011). Spatializing the social networks of gangs to explore patterns of violence. Journal of Quantitative Criminology, 27(4), 521–545.Find this resource:

Tracy, M., Braga, A. A., & Papachristos, A. V. (2016). The transmission of gun and other weapon-involved violence within social networks. Epidemiologic Reviews, 38(1), 70–86.Find this resource:

Tremblay, P. (1993). Searching for suitable co-offenders. In R. V. Clarke & M. Felson (Eds.), Routine activities and rational choice (pp. 17–36). New Brunswick, NJ: Transaction.Find this resource:

Tremblay, P., Charest, M., Charette, Y., & Tremblay-Faulkner, M. (2016). Le délinquant affilié: La sous-culture des gangs de rue haïtiens de Montréal. Montreal: Liber. [The connected delinquent: Montreal’s Haitian street gang subculture].Find this resource:

Tremblay, P., Talon, B., & Hurley, D. (2001). Body switching and related adaptations in the resale of stolen vehicles: Script elaborations and aggregate crime learning curves. British Journal of Criminology, 41(4), 561–579.Find this resource:

van Mastrigt, S. B., & Carrington, P. J. (2014). Sex and age homophily in co-offending networks: Opportunity or preference? In C. Morselli (Ed.), Crime and networks (pp. 28–51). New York: Routledge.Find this resource:

Warr, M. (2002). Companions in crime: The social aspect of criminal conduct. New York: Cambridge University Press.Find this resource:

Warr, M., & Stafford, M. (1991). The influence of delinquent peers: What they think or what they do? Criminology, 29(4), 851–866.Find this resource:

Watts, D. J. (2003). Six degrees: The science of a connected age. New York: W. W. Norton.Find this resource:

Weerman, F. M. (2003). Co-offending as social exchange: Explaining characteristics of co-offending. British Journal of Criminology, 43(2), 398–416.Find this resource:

Weerman, F. M. (2011). Delinquent peers in context: A longitudinal network analysis of selection and influence effects. Criminology, 49(1), 253–286.Find this resource:

Weerman, F. M., Wilcox, P., & Sullivan, C. J. (2017). The short-term dynamics of peers and delinquent behavior: An analysis of bi-weekly changes within a high school student network. Journal of Quantitative Criminology. Advance online publication.Find this resource:

Young, J. T. (2011). How do they “end up together”? A social network analysis of self-control, homophily, and adolescent relationships. Journal of Quantitative Criminology, 27(3), 251–273.Find this resource:

Young, J. T. (2014). “Role magnets”? An empirical investigation of popularity trajectories for life-course persistent individuals during adolescence. Journal of Youth and Adolescence, 43(1), 104–115.Find this resource:

Young, J. T., Barnes, J. C., Meldrum, R. C., & Weerman, F. M. (2011). Assessing and explaining misperceptions of peer delinquency. Criminology, 49(2), 599–630.Find this resource: