Abstract
Social networks have long been central to some of the most influential theories in criminology. For researchers interested in exploring social networks (or personal networks) and their relationship to crime, network analysis provides the leverage to answer questions in a more refined way than do nonrelational analyses. Network approaches are gaining popularity in criminology, but the formal use of network techniques and methods remains limited. After briefly discussing the background of network analysis, as well as important issues related to sampling, this chapter uses a hypothetical dataset to illustrate the utility of social network graphs and measures, both for theory and policy.
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Notes
- 1.
In contrast to a social network, a personal, or egocentric, network focuses on one node of interest (i.e., ego) and its alters (i.e., associates).
- 2.
Similarly, nodes can be of many different types, including individuals, organizations, countries, and groups.
- 3.
This hypothetical example is similar to the work of Natarajan (2006), which used wiretapping information to study network attributes of a heroin distribution group in New York.
- 4.
In addition to adjacency matrices, there are also incident matrices, in which the rows are the nodes and the columns are incidents, events, or affiliations (i.e., the value in a cell would indicate whether a particular node was part of that specific incident, event, or affiliated with that specific group).
- 5.
If the value of the tie does not reflect the strength of some relationship, but instead some combination of relationships (i.e., the network is multirelational), researchers also have the option of determining the density for the network across each type of relationship.
- 6.
There are also n-cliques, which focus on geodesic distances (i.e., the shortest path between two nodes). A 1-clique would be a subgroup in which all geodesic distances among the members is 1 (i.e., a traditional clique). A 2-clique would be a subgroup in which nodes were connected to each other directly or indirectly through another node (thus, the largest geodesic distance is 2). For more information about n-cliques and other cohesive subgroups, see Scott (2000) and Wasserman and Faust (1994).
- 7.
The six cliques contain the following nodes: (1) 3,7,9,11; (2) 3,7,9,13; (3) 2,3,5,11; (4) 7,9,15; (5) 7,11,14; and (6)Â 2,11,14.
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McGloin, J.M., Kirk, D.S. (2010). Social Network Analysis. In: Piquero, A., Weisburd, D. (eds) Handbook of Quantitative Criminology. Springer, New York, NY. https://doi.org/10.1007/978-0-387-77650-7_11
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