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Early Identification of Violent Criminal Gang Members

Authors:

Elham Shaabani,

Paulo Shakarian,

John BertettoAuthors Info & Claims

KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 2079 - 2088

https://doi.org/10.1145/2783258.2788618

Published: 10 August 2015 Publication History

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.

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References

[1]

J. Bertetto. Countering criminal street gangs: Lessons from the counterinsurgent battlespace. Law Enforcement Executive Forum, 12(3):43, 2012.

[2]

A. Braga, D. Hureau, and A. Papachristos. Deterring gang-involved gun violence: Measuring the impact of boston's operation ceasefire on street gang behavior. Journal of Quantitative Criminology, pages 1--27, 2013.

[3]

P. Brantingham and P. Brantingham. Crime Pattern Theory. In R. Wortley and L. Mazerolle, editors, Enviromental Criminology and Crime Analysis, pages 78--93. 2008.

[4]

N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16(1):321--357, 2002.

Digital Library

[5]

P. Expert, T. S. Evans, V. D. Blondel, and R. Lambiotte. Uncovering space-independent communities in spatial networks. Proceedings of the National Academy of Sciences, 108(19):7663--7668, 2011.

[6]

L. C. Freeman. A set of measures of centrality based on betweenness. Sociometry, 40(1):pp. 35--41, 1977.

[7]

W. Gorr and R. Harries. Introduction to crime forecasting. International Journal of Forecasting, 19(4):551 -- 555, 2003.

[8]

M. Granovetter. Threshold models of collective behavior. The American Journal of Sociology, (6):1420--1443.

[9]

H. Han, W.-Y. Wang, and B.-H. Mao. Borderline-smote: a new over-sampling method in imbalanced data sets learning. In Advances in intelligent computing, pages 878--887. Springer, 2005.

Digital Library

[10]

J. C. Howell. Gangs in America's Communities. Sage, 2012.

[11]

M. Kitsak, L. K. Gallos, S. Havlin, F. Liljeros, L. Muchnik, H. E. Stanley, and H. A. Makse. Identification of influential spreaders in complex networks. Nat Phys, 6(11):888--893, Nov. 2010.

[12]

H. Liu and D. E. Brown. Criminal incident prediction using a point-pattern-based density model. International Journal of Forecasting, 19(4):603--622, Oct. 2003.

[13]

J. M. Mcgloin, C. J. Sullivan, A. R. Piquero, and S. Bacon. Investigating the stability of co-offending and co-offenders among a sample of youthful offenders. Criminology, 46(1):155--188, 2008.

[14]

C. Morselli. Inside Criminal Networks. Springer, 2009.

[15]

C. Overall and G. Day. The Hammer Gang: an exercise in spatial analyis of an armed robbery series using the probability grid method. In S. Chainey and L. Tompson, editors, Crime Mapping Case Studies, pages 55--62. 2008.

[16]

A. Papachristos, C. Wildeman, and E. Roberto. Tragic, but not random: The social contagion of nonfatal gunshot injuries. Social Science and Medicine, page 139.

[17]

B. A. H. D. Papachristos, A. Social networks and the risk of gunshot injury. J. Urban Health, 2012.

[18]

H. D. B. A. Papachristos, A. The corner and the crew: The influence of geography and social networks on gang violence. American Sociological Review, 2013.

[19]

D. Paulo, B. Fischl, T. Markow, M. Martin, and P. Shakarian. Social network intelligence analysis to combat street gang violence. In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM '13, pages 1042--1049, New York, NY, USA, 2013. ACM.

Digital Library

[20]

R. R. Petersen and U. K. Wiil. Crimefighter investigator: A novel tool for criminal network investigation. In EISIC, pages 197--202. IEEE, 2011.

Digital Library

[21]

D. K. Rossmo and S. Rombouts. Geographic Profiling. In R. Wortley and L. Mazerolle, editors, Enviromental Criminology and Crime Analysis, pages 136--149. 2008.

[22]

J. Schroeder, J. Xu, and H. Chen. Crimelink explorer: Using domain knowledge to facilitate automated crime association analysis. In H. Chen, R. Miranda, D. Zeng, C. Demchak, J. Schroeder, and T. Madhusudan, editors, Intelligence and Security Informatics, volume 2665 of Lecture Notes in Computer Science, pages 168--180. Springer Berlin Heidelberg, 2003.

Digital Library

[23]

S. B. Seidman. Network structure and minimum degree. Social Networks, 5(3):269 -- 287, 1983.

[24]

P. Shakarian, J. Salmento, W. Pulleyblank, and J. Bertetto. Reducing gang violence through network influence based targeting of social programs. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '14, pages 1829--1836, New York, NY, USA, 2014. ACM.

Digital Library

[25]

M. A. Tayebi, M. Ester, U. Gl\"asser, and P. L. Brantingham. Spatially embedded co-offence prediction using supervised learning. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '14, pages 1789--1798, New York, NY, USA, 2014. ACM.

Digital Library

[26]

M. A. Tayebi, M. Jamali, M. Ester, U. Gl\"asser, and R. Frank. Crimewalker: A recommendation model for suspect investigation. In Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys '11, pages 173--180, New York, NY, USA, 2011. ACM.

Digital Library

[27]

D. J. Watts and S. H. Strogatz. Collective dynamics of "small-world"networks. nature, 393(6684):440--442, 1998.

Cited By

Martinez RSquire K(2024)Engaging recently incarcerated and gang affiliated Black and Latino/a young adults in designing social collocated applications for mixed reality smart glasses through community-based participatory design workshops.Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642895(1-17)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642895
Xu WHan PDuan SLiu C(2022)FIGAT: Accurately Classify Individual Crime Risks with Multi-information FusionIEEE Transactions on Services Computing10.1109/TSC.2022.3206093(1-14)Online publication date: 2022
https://doi.org/10.1109/TSC.2022.3206093
Ren LHu RLi DWu JZang YHu W(2022)Cross-Regional Friendship Inference via Category-Aware Multi-Bipartite Graph Embedding2022 IEEE 47th Conference on Local Computer Networks (LCN)10.1109/LCN53696.2022.9843580(73-80)Online publication date: 26-Sep-2022
https://doi.org/10.1109/LCN53696.2022.9843580
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Index Terms

Early Identification of Violent Criminal Gang Members
1. Applied computing
  1. Law, social and behavioral sciences
    1. Sociology

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Information

Published In

cover image ACM Conferences

KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2015

2378 pages

ISBN:9781450336642

DOI:10.1145/2783258

General Chairs:
Longbing Cao
University of Technology, Sydney
,
Chengqi Zhang
University of Technology, Sydney
,
Program Chairs:
Thorsten Joachims
Cornell University
,
Geoff Webb
Monash University
,
Dragos D. Margineantu
Boeing Research
,
Graham Williams
Australian Taxation Office

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 10 August 2015

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KDD '15

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KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 10 - 13, 2015

NSW, Sydney, Australia

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KDD '15 Paper Acceptance Rate 160 of 819 submissions, 20%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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KDD '25

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August 3 - 7, 2025

Toronto , ON , Canada

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Cited By

Martinez RSquire K(2024)Engaging recently incarcerated and gang affiliated Black and Latino/a young adults in designing social collocated applications for mixed reality smart glasses through community-based participatory design workshops.Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642895(1-17)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642895
Xu WHan PDuan SLiu C(2022)FIGAT: Accurately Classify Individual Crime Risks with Multi-information FusionIEEE Transactions on Services Computing10.1109/TSC.2022.3206093(1-14)Online publication date: 2022
https://doi.org/10.1109/TSC.2022.3206093
Ren LHu RLi DWu JZang YHu W(2022)Cross-Regional Friendship Inference via Category-Aware Multi-Bipartite Graph Embedding2022 IEEE 47th Conference on Local Computer Networks (LCN)10.1109/LCN53696.2022.9843580(73-80)Online publication date: 26-Sep-2022
https://doi.org/10.1109/LCN53696.2022.9843580
Ren LHu RLi DWang ZWu JLi XHu W(2022)Who is your friend: inferring cross-regional friendship from mobility profilesMultimedia Tools and Applications10.1007/s11042-022-13672-882:8(12719-12737)Online publication date: 19-Sep-2022
https://doi.org/10.1007/s11042-022-13672-8
Yu XHan PLiu CDuan SPan HXiang X(2021)FIGAT: Accurately Predict Individual Crime Risks with Multi-information Fusion2021 IEEE Sixth International Conference on Data Science in Cyberspace (DSC)10.1109/DSC53577.2021.00041(248-255)Online publication date: Oct-2021
https://doi.org/10.1109/DSC53577.2021.00041
Nurek MMichalski R(2020)Combining Machine Learning and Social Network Analysis to Reveal the Organizational StructuresApplied Sciences10.3390/app1005169910:5(1699)Online publication date: 2-Mar-2020
https://doi.org/10.3390/app10051699
Michalski RDziubaltowska DMacek P(2020)Revealing the Character of Nodes in a Blockchain With Supervised LearningIEEE Access10.1109/ACCESS.2020.30016768(109639-109647)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3001676
Huang SBan T(2019)A Topic-Based Unsupervised Learning Approach for Online Underground Market Exploration2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE)10.1109/TrustCom/BigDataSE.2019.00036(208-215)Online publication date: Aug-2019
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Zhong TLiu FZhou FTrajcevski GZhang K(2019)Motion Based Inference of Social Circles via Self-Attention and Contextualized EmbeddingIEEE Access10.1109/ACCESS.2019.29155357(61934-61948)Online publication date: 2019
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Yang CZhong LLi LJie LBarrett RCummings RAgichtein EGabrilovich E(2017)Bi-directional Joint Inference for User Links and Attributes on Large Social GraphsProceedings of the 26th International Conference on World Wide Web Companion10.1145/3041021.3054181(564-573)Online publication date: 3-Apr-2017
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