@inproceedings{ee6850569ddb49b38c3749742cbb43c1,
title = "Early identification of violent criminal gang members",
abstract = "Gang violence is a major problem in the United States accounting for a large fraction of homicides and other violent crime. In this paper, we study the problem of early identification of violent gang members. Our approach relies on modified centrality measures that take into account additional data of the individuals in the social network of co-arrestees which together with other arrest metadata provide a rich set of features for a classification algorithm. We show our approach obtains high precision and recall (0.89 and 0.78 respectively) in the case where the entire network is known and out-performs current approaches used by law-enforcement to the problem in the case where the network is discovered overtime by virtue of new arrests - mimicking real-world law-enforcement operations. Operational issues are also discussed as we are preparing to leverage this method in an operational environment.",
keywords = "Criminology, Social network analysis",
author = "Elham Shaabani and Ashkan Aleali and Paulo Shakarian and John Bertetto",
note = "Publisher Copyright: {\textcopyright} 2015 ACM.; 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015 ; Conference date: 10-08-2015 Through 13-08-2015",
year = "2015",
month = aug,
day = "10",
doi = "10.1145/2783258.2788618",
language = "English (US)",
series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
publisher = "Association for Computing Machinery",
pages = "2079--2088",
booktitle = "KDD 2015 - Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining",
}