Criminal Community Detection Based on Isomorphic Subgraph Analytics

  • 1 School of Computer Science and Engineering (SCE), Taylor’s University, Selangor, Subang Jaya, Malaysia
  • 2 School of Computer Science and Engineering (SCE), Taylor’s University, Selangor, Subang Jaya, Malaysia
  • 3 School of Computer Science and Engineering (SCE), Taylor’s University, Selangor, Subang Jaya, Malaysia


All highly centralised enterprises run by criminals do share similar traits, which, if recognised, can help in the criminal investigative process. While conducting a complex confederacy investigation, law enforcement agents should not only identify the key participants but also be able to grasp the nature of the inter-connections between the criminals to understand and determine the modus operandi of an illicit operation. We studied community detection in criminal networks using the graph theory and formally introduced an algorithm that opens a new perspective of community detection compared to the traditional methods used to model the relations between objects. Community structure, generally described as densely connected nodes and similar patterns of links is an important property of complex networks. Our method differs from the traditional method by allowing law enforcement agencies to be able to compare the detected communities and thereby be able to assume a different viewpoint of the criminal network, as presented in the paper we have compared our algorithm to the well-known Girvan-Newman. We consider this method as an alternative or an addition to the traditional community detection methods mentioned earlier, as the proposed algorithm allows, and will assists in, the detection of different patterns and structures of the same community for enforcement agencies and researches. This methodology on community detection has not been extensively researched. Hence, we have identified it as a research gap in this domain and decided to develop a new method of criminal community detection.

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  • [1] Cai, Q., Ma, L., Gong, M., & Tian, D., A survey on network community detection based on evolutionary computation. International Journal of Bio-Inspired Computation, Vol. 8(2), pp. 84-98, January, 2016.

  • [2] Lim, M., Abdullah, A., Jhanjhi, N., Khan, M. K., & Supramaniam, M. (2019). Link Prediction in Time-Evolving Criminal Network With Deep Reinforcement Learning Technique. IEEE Access, 7, 184797-184807.

  • [3] Zhang, Chi, and Osmar R. Zaïane., Detecting local communities in networks with edge uncertainty, IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Barcelona, Spain, 2018. pp. 9-16.

  • [4] Sangkaran, T., Abdullah, A., Jhanjhi, NZ., & Supramaniam, M., Survey on isomorphic graph algorithms for graph analytics, International Journal of Computer Science and Network Security, Vol 19(1), pp. 85-92, January, 2019.

  • [5] Ullmann, J. R., An algorithm for subgraph isomorphism. Journal of the Association for Computer Machinery, Vol 23(1), pp.31-42, January, 1976.

  • [6] Cordella, L. P., Foggia, P., Sansone, C., & Vento, M. Performance evaluation of the VF graph matching algorithm. IEEE Proceedings 10th International Conference on Image Analysis and Processing, Venice, Italy, pp. 1172-1177, September, 1999.

  • [7] Cordella, L. P., Foggia, P., Sansone, C., & Vento, M.. A (sub) graph isomorphism algorithm for matching large graphs, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 26(10), pp. 1367-1372, August 2004.

  • [8] Schmidt, D. C., & Druffel, L. E., A fast backtracking algorithm to test directed graphs for isomorphism using distance matrices, Journal of the Association for Computer Machinery, Vol 23(3), pp. 433-445. July, 1976

  • [9] Corneil, D. G., & Gotlieb, C. C., An eflcient algorithm for graph isomorphism, Journal of the Association for Computer Machinery, Vol 17(1), pp.51-64, January, 1970

  • [10] Fu, L., & Chandra, S., Optimized backtracking for subgraph isomorphism. International Journal of Database Management Systems, Vol 4(6), pp. 1. December, 2012.

  • [11] Javed, M. A., Younis, M. S., Latif, S., Qadir, J., & Baig, A., Community detection in networks: A multidisciplinary review. Journal of Network and Computer Applications, Vol 108, pp. 87-111, April, 2018.

  • [12] Lim, M., Abdullah, A., & Jhanjhi, N. Z. (2019). Performance optimization of criminal network hidden link prediction model with deep reinforcement learning. Journal of King Saud University-Computer and Information Sciences.

  • [13] M. Lim, A. Abdullah, NZ. Jhanjhi and M. K. Khan, " A Deep Reinforcement Learning Data Fusion model for Time-evolving Criminal Network Link Prediction," in IEEE Access. doi: 10.1109/ACCESS.2019.2958873, 2020.

  • [14] Li, J., Lear, M. J., Kawamoto, Y., Umemiya, S., Wong, A. R., Kwon, E., ... & Hayashi, Y. Oxidative amidation of nitroalkanes with amine nucleophiles using molecular oxygen and iodine. Angewandte Chemie International Edition, Vol 54(44), pp. 12986-12990, October, 2015.

  • [15] Newman, M. E., Community detection in networks: Modularity optimization and maximum likelihood are equivalent. arXiv preprint arXiv:1606.02319, June, 2016.

  • [16] Hu, R., Andreas, J., Rohrbach, M., Darrell, T., & Saenko, K. Learning to reason: End-to-end module networks for visual question answering. IEEE International Conference on Computer Vision, Venice, Italy, 2017, pp. 804-813.

  • [17] Newman, M. E., & Reinert, G., Estimating the number of communities in a network, Physical Review Letters, Vol. 117(7), pp. 078301, August, 2016.

  • [18] Krzakala, F., Moore, C., Mossel, E., Neeman, J., Sly, A., Zdeborová, L., & Zhang, P., Spectral redemption in clustering sparse networks, Proceedings of the National Academy of Sciences, Vol.110(52), pp.20935-20940, December, 2013.

  • [19] Liu, C., & Liu, Q., Community detection based on differential evolution using modularity density, Information 9, Vol (9), pp. 218, September, 2018.

  • [20] Magalingam, P., Davis, S., & Rao, A., Using shortest path to discover criminal community, Digital Investigation, Vol. 15, pp. 1-17, December, 2015.

  • [21] Bahulkar, A., Szymanski, B. K., Baycik, N. O., & Sharkey, T. C., Community detection with edge augmentation in criminal networks, IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Barcelona, Spain,2018, pp. 1168-1175.

  • [22] Calderoni, F., & Piccardi, C., Uncovering the structure of criminal organizations by community analysis: The infinito network, Tenth International Conference on Signal-Image Technology and Internet-Based Systems, Marrakech, Morocco, 2014, pp. 301-308

  • [23] Carletti, V., Foggia, P., Saggese, A., & Vento, M., Challenging the time complexity of exact subgraph isomorphism for huge and dense graphs with VF3, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 40(4), pp. 804-818, November, 2014.

  • [24] Omidvar, M. N., Li, X., Mei, Y., & Yao, X. Cooperative co-evolution with differential grouping for large scale optimization. IEEE Transactions on Evolutionary Computation, Vol 18(3), pp. 378-393, September, 2013.

  • [25] Huang, Q., White, T., Jia, G., Musolesi, M., Turan, N., Tang, K., & Yao, X., Community detection using cooperative co-evolutionary differential evolution, 2012 International Conference on Parallel Problem Solving from Nature, Berlin, Germany, pp. 235-244

  • [26] McKay, B. D., & Piperno, A., Practical graph isomorphism, II. Journal of Symbolic Computation, Vol. 60, pp. 94-112, January, 2014.

  • [27] Dado, M., & Bodemer, D. A review of methodological applications of social network analysis in computer-supported collaborative learning. Educational Research Review, Vol.22, pp. 159-180.

  • [28] Farine, Damien. The dynamics of transmission and the dynamics of networks. Journal of Animal Ecology 86, Vol.3, pp.415-418. May 2017

  • [29] Lim, M., Abdullah, A., Jhanjhi, NZ., & Supramaniam, M., Hidden Link Prediction in Criminal Networks Using the Deep Reinforcement Learning Technique. Computers, Vol 8(1), pp 8, March, 2017

  • [30] Sangkaran, Theyvaa, Azween Abdullah, and N. Z. JhanJhi. "Criminal Network Community Detection Using Graphical Analytic Methods: A Survey." doi: 10.4108/eai.13-7-2018.162690

  • [31] Ozgul, F., Erdem, Z., Bowerman, C., & Bondy, J. (2010, June). Combined detection model for criminal network detection. In Pacific-Asia Workshop on Intelligence and Security Informatics (pp. 1-14). Springer, Berlin, Heidelberg.

  • [32] Ozgul, F., Gok, M., Erdem, Z., & Ozal, Y. (2012, June). Detecting criminal networks: SNA models are compared to proprietary models. In 2012 IEEE International Conference on Intelligence and Security Informatics (pp. 156-158). IEEE.

  • [33] Nowé, A., Vrancx, P., & De Hauwere, Y. M. (2012). Game theory and multi-agent reinforcement learning. In Reinforcement Learning (pp. 441-470). Springer, Berlin, Heidelberg.

  • [34] Barbosa, S. E., & Petty, M. D. (2014). Reinforcement learning in an environment synthetically augmented with digital pheromones. Advances in Artificial Intelligence, 2014.


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