6 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationKDD 2014 - Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages1829-1836
Number of pages8
ISBN (Print)9781450329569
DOIs
StatePublished - Jan 1 2014
Event20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014 - New York, NY, United States
Duration: Aug 24 2014Aug 27 2014

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014
CountryUnited States
CityNew York, NY
Period8/24/148/27/14

Keywords

  • complex networks
  • network diffusion
  • propagation in networks

ASJC Scopus subject areas

  • Software
  • Information Systems

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    Shakarian, P., Salmento, J., Pulleyblank, W., & Bertetto, J. (2014). Reducing gang violence through network influence based targeting of social programs. In KDD 2014 - Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1829-1836). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). Association for Computing Machinery. https://doi.org/10.1145/2623330.2623331