In This Article Expand or collapse the "in this article" section Social Networks

  • Introduction
  • Law Enforcement and Counter-Terrorism Applications
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Criminology Social Networks
by
Peter J. Carrington
  • LAST REVIEWED: 30 March 2015
  • LAST MODIFIED: 30 March 2015
  • DOI: 10.1093/obo/9780195396607-0141

Introduction

This article is concerned with applications in criminology of the concept of the social network. Three distinct types of networks occur in criminological theory and research. First, the family of theories including differential association, social learning, and peer influence proposes that the people with whom one interacts, such as friends, family, etc., have an influence on one’s criminality. The notion of “the people with whom one interacts” is precisely captured by the concept in social network analysis of the personal network. Thus, some researchers have conceptualized the theories of differential association, peer influence, etc. as being concerned with the influence of an individual’s personal network on his or her criminality. This kind of research (and theorizing) is mainly, though not entirely, concerned with the delinquency of children and adolescents rather than adult crime, and is often referred to as “peer network” or “peer influence” research, although the networks are not necessarily restricted to peers. Second, there is a small literature on the role of neighborhood networks in suppressing or, less commonly, encouraging crime in the neighborhood, mainly based on collective efficacy theory. Finally, the criminal network is distinguished from personal and neighborhood networks in two principal ways. First, it consists, by definition, entirely of criminals. Second, the theoretical interest of criminal networks is not (normally) in the influence of the network on an individual or neighborhood, but in the criminal network as a form of organization of criminal groups and criminal activities. Thus, research on criminal networks tends to be concerned with describing their composition and structure and relating these to success in achieving criminal objectives, such as the survival, efficiency, or profitability of a criminal enterprise; or the longevity, profitability, or local dominance of a street gang. The unit of analysis in criminal network research is usually, though not always, the group rather than the individual; for example, youth gangs or street gangs, organized crime groups, or the trafficking networks involved in criminal transactions such as the smuggling or distribution of illicit goods. By explicitly adopting the concept of the network, criminologists have been able to draw on an array of insights and analytic methods that have been developed over the past eighty years in the field of social network analysis.

General Overviews

Since its appearance in the 1930s in the form of sociometry, social network analysis (SNA) has become a major paradigm for social research in such areas as communication, organizations, markets, community, the family and marriage, small group dynamics, social support, social mobility, and animal behavior, to name a few. It is used by researchers in the disciplines of sociology, social anthropology, social psychology, political science, history, communication science, economics, epidemiology, criminology, ethnology, ethology, physics, information science, etc. At the heart of SNA are three insights, or assumptions: that social relations are more important than individual attributes in understanding human society; that the structure of social relations is more important than their content; and that social relations can be represented by graphs of points and lines, which can then be analyzed visually or by using the concepts, theorems, and methods of graph theory and semigroup algebra. Thus, the underlying mathematics of SNA are relational, not quantitative. Like any mathematical approach to social research, social network analysis strips away the unique details of social situations to reveal, or model, the bare structural essentials. Seeing these similarities enables the researcher to benefit from insights from many radically different fields of study: recently Albert-László Barabási, a physicist, pointed out that friendship networks share the property of extreme inequality of degree with many other types of networks, such as computer networks and air traffic networks: that is, a few people have many more friends than do others, and people with many friends are more likely to acquire new friends. Computers with many connections are termed hubs; as are airports that are connected to many other airports. The same mathematical network model describes this aspect of computer networks, air traffic networks, and friendship networks; and lessons learned from one of these areas of research can potentially be used in the others. In summary, SNA is a fundamentally relational, or structural, approach to social theory, and a mathematical approach to modeling data.

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