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SETSCI - Volume 1 (2017)
ISMSIT2017 - International Symposium on Multidisciplinary Studies and Innovative Technologies, Tokat, Turkey, Dec 02, 2017

Community Detection in Social Media Network with Maximum Modularity Using Girvan-Newman Algorithm (ISMSIT2017_51)
Ali Fatih Gündüz1*, Ahmet Karadoğan2
1İnönü University, Malatya, Turkey
2İnönü University, Malatya, Turkey
* Corresponding author: fatih.gunduz@inonu.edu.tr
Published Date: 2017-12-08   |   Page (s): 222-225   |    182     3

ABSTRACT Social networks are formed from interactions of peoples. Measuring the degree of those relationships requires interpreting connectivity of vertices and extracting information from it. Generally individuals form smaller sub-communities in those networks. Identifying those communities by determining sizes of cliques is a challenge and there are numerous solutions in the literature for this problem. In this study we reviewed Girvan-Newman community detection algorithm and applied it on a real life social network obtained from Twitter data. Friendship relations of students of four different universities were used to form the network. A connected graph is generated from this data set in which the students are represented as vertices and followership relations of the students formed the edges of the graph. Since those universities are geographically close to each other, the graph consisted of different link connections among those four clusters. Then community clusters were detected in this connected graph by using Girvan-Newman community detection algorithm
KEYWORDS Community detection, Girvan-Newman, data mining, Twitter, social media, social network, clustering
REFERENCES [1] Hill, Russell A., and Robin IM Dunbar. "Social network size in humans." Human nature 14.1 (2003): 53-72.

[2] Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, Etienne Lefebvre, Fast unfolding of communities in large networks, in Journal of Statistical Mechanics: Theory and Experiment 2008 (10), P1000

[3] M. Girvan and M. E. J. Newman. Community structure in social and biological networks. Physical Sciences - Applied Mathematics, 2002.

[4] M. E. J. Newman and M. Girvan. Finding and evaluating community structure in networks. Phys. Rev. E, 69(2):026113, February 2004.

[5] Newman M. E., Mark E. J., Girvan M., (2004), "Finding and evaluating community structure in networks", Physical Review E, 69 (2), 026113.

[6] M. Newman. 2006. Modularity and community structure in networks. Proceedings of the National Academy of Sciences, vol. 103, no. 23, pp. 85778582.

[7] Gunes, Ismail, and Haluk Bingol. "Community detection in complex networks using agents." arXiv preprint cs/0610129 (2006).

[8] Freeman, L., (1977). A set of measures of centrality based upon betweenness. In Sociometry 40:35-41.

[9] Kleinfeld, Judith. "Could it be a big world after all? The six degrees of separation myth." Society, April 12 (2002): 5-2.

[10] Ressler, Steve. "Social network analysis as an approach to combat terrorism: Past, present, and future research." Homeland Security Affairs 2.2 (2006).

[11] Krebs, Valdis E. "Mapping networks of terrorist cells." Connections24.3 (2002): 43-52.

[12] Xu, Jennifer, and Hsinchun Chen. "Criminal network analysis and visualization." Communications of the ACM 48.6 (2005): 100-107.

[13] Watts, Duncan J. "Networks, dynamics, and the small-world phenomenon." American Journal of sociology 105.2 (1999): 493-527.

[14] de Sola Pool, Ithiel, and Manfred Kochen. "Contacts and influence." Social networks 1.1 (1978): 5-51.

[15] Milgram, Stanley. "Six degrees of separation." Psychology Today 2 (1967): 60-64.

[16] Clauset, A., Newman , M.E.J., Moore, C., (2004). Finding community structure in very large networks. In Physical Review E, 70:061111.

[17] Tasgin, Mursel, Amac Herdagdelen, and Haluk Bingol. "Community detection in complex networks using genetic algorithms." arXiv preprint arXiv:0711.0491 (2007).

[18] Y. Atay, I. Koc, I. Babaoglu, ve H. Kodaz, “Community detection from biological and social networks: A comparative analysis of metaheuristic algorithms”, Applied Soft Computing, c. 50, ss. 194–211, Oca. 2017.

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