Analysis of two crime-related networks derived from bipartite social networks

Abstract

In this paper we investigate two real crime-related networks, which are both bipartite. The bipartite networks are: a spatial network where crimes of various types are committed in different local government areas; and a dark terrorist network where individuals attend events or have common affiliations. In each case we analyse the communities found by a random-walk based algorithm in the primary weighted projection network. We demonstrate that the identified communities represent meaningful information, and in particular, that the small communities found in the terrorist network represent meaningful cliques.