Abstract
Social networking has become one of the most useful tools in modern society. Unfortunately, terrorists are taking advantage of the easiness of accessing social networks and they have set up profiles to recruit, radicalize and raise funds. Most of these profiles have pages that existing as well as new recruits to join the terrorist groups see and share information. Therefore, there is a potential need of detecting terrorist communities in social network in order to search for key hints in posts that appear to promote the militants cause. Community detection has recently drawn intense research interest in diverse ways. However, it represents a big challenge of practical interest that has received a great deal of attention. Social network clustering allows the labeling of social network profiles that is considered as an important step in community detection process. In this paper, we used possibilistic c-means algorithm for clustering a set of profiles that share some criteria. The use of possibility theory version of k-means algorithm allows more flexibility when assigning a social network profile to clusters. We experimentally showed the efficiency of the use of possibilistic c-means algorithm through a detailed tweet extract, semantic processing and classification of the community detection process.
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Bastian, M., Heymann, S., Jacomy, M.: Gephi: An open source software for exploring and manipulating networks (2009)
Benferhat, S., Dubois, D., Kaci, S., Prade, H.: Modeling positive and negative information in possibility theory. Int. J. Inf. Syst. (IJIS) 23(10), 1094–1118 (2008)
Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70, 066111 (2004)
Correa Farias, C., Valero Ubierna, C., Barreiro Elorza, P., Diago Santamaria, M.P., Tardaguila Laso, J.: A comparison of fuzzy clustering algorithms applied to feature extraction on vineyard, October 2011
Cranmer, S.J., Desmarais, B.A.: Inferential network analysis with exponential random graph models. Polit. Anal. 19(1), 66–86 (2011)
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99(12), 7821–7826 (2002)
Golsefid, S.M.M., Zarandi, M.H.F., Bastani, S.: Fuzzy duocentric community detection model in social networks. Soc. Netw. 43, 177–189 (2015)
Gomez, D., Rodriguez, J.T., Yanez, J., Montero, J.: A new modularity measure for fuzzy community detection problems based on overlap and grouping functions. Int. J. Approximate Reasoning 74, 88–107 (2016)
Jackson, M.O., Lpez-Pintado, D.: Diffusion and contagion in networks with heterogeneous agents and homophily. Netw. Sci. 1, 49–67 (2013). 4
Jain, A.K.: Data clustering: 50 years beyond k-means. Pattern Recogn. Lett. 31(8), 651–666 (2010). Award winning papers from the 19th International Conference on Pattern Recognition (ICPR)
Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An efficient k-means clustering algorithm: Analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 881–892 (2002)
Li, W., Schuurmans, D.: Modular community detection in networks. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI 2011, vol. 2, pp. 1366–1371. AAAI Press (2011)
Maas, A.L., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., Potts, C.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, HLT 2011, vol. 1, pp. 142–150. Association for Computational Linguistics, Stroudsburg (2011)
Newman, M.E.J.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577–8582 (2006)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets. Cambridge University Press, Cambridge (2011)
Schlauch, W.E., Zweig, K.A.: Influence of the null-model on motif detection. In: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, ASONAM 2015, pp. 514–519. ACM, New York (2015)
Steinhaeuser, K., Chawla, N.V.: Identifying and evaluating community structure in complex networks. Pattern Recogn. Lett. 31(5), 413–421 (2010)
Sun, P.G.: Community detection by fuzzy clustering. Phys. A 419, 408–416 (2015)
Timm, H., Borgelt, C., Döring, C., Kruse, R.: Fuzzy cluster analysis with cluster repulsion. In: Proceedings of the European Symposium on Intelligent Technologies, Hybrid Systems and Their Implementation on Smart Adaptive Systems, eunite 2001, Puerto de la Cruz, Tenerife, Spain. Verlag Mainz, Aachen (2001)
Wang, M., Wang, C., Yu, J.X., Zhang, J.: Community detection in social networks: an in-depth benchmarking study with a procedure-oriented framework. Proc. VLDB Endow. 8(10), 998–1009 (2015)
Yang, M.-S., Wu, K.-L.: Unsupervised possibilistic clustering. Pattern Recogn. 39(1), 5–21 (2006)
Zhang, S., Wang, R.-S., Zhang, X.-S.: Identification of overlapping community structure in complex networks using fuzzy-means clustering. Phys. A 374(1), 483–490 (2007)
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Moussaoui, M., Zaghdoud, M., Akaichi, J. (2018). Clustering Social Network Profiles Using Possibilistic C-means Algorithm. In: De Pietro, G., Gallo, L., Howlett, R., Jain, L. (eds) Intelligent Interactive Multimedia Systems and Services 2017. KES-IIMSS-18 2018. Smart Innovation, Systems and Technologies, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-319-59480-4_42
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DOI: https://doi.org/10.1007/978-3-319-59480-4_42
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