Reducing gang violence through network influence based targeting of social programs

In this paper, we study a variant of the social network maximum influence problem and its application to intelligently approaching individual gang members with incentives to leave a gang. The goal is to identify individuals who when influenced to leave gangs will propagate this action. We study this emerging application by exploring specific facets of the problem that must be addressed when modeling this particular situation. We formulate a new influence maximization variant - the "social incentive influence" (SII) problem and study it both formally and in the context of the law-enforcement domain. Using new techniques from unconstrained submodular maximization, we develop an approximation algorithm for SII and present a suite of experimental results - including tests on real-world police data from Chicago.