Techniques to Interdict and Monitor Cohesiveness in Dark Networks

William Marsh Rice University, Houston TX

Investigators

Abstract

Dark social networks consist of illegal and covert networks of people. Given the nature of being "dark", it is vital to disrupt the network through capture (interdiction) or intelligence gathering (monitoring). In contrast, information on these networks is often incomplete, inaccurate or not available. Gathering information on dark networks dates back to World War II and has become more prevalent today given the current climate of "The War on Terror"; the increased access to information; and the usage of social network analysis to analyze these networks. This award supports fundamental research using innovative mathematical techniques to interdict or monitor cohesiveness within dark social networks. This award also supports a concerted effort to engage underrepresented groups within the research and positively impact engineering education. In particular, the research team will utilize new techniques within branch decomposition methods and integer programming to investigate the clique-interdiction problem, the minimum clique-transversal problem and the variations of the problems where "clique" is replaced with k-plex, k-clique or k-club. This research will advance the knowledge base of combinatorial optimization and integer programming while also contributing to the increased scalability and efficiency for solving computationally hard problems related to homeland security, criminal justice, and marketing.