Abstract
Many computer science problems are structured as a network. Mobile, e-mail, social networks (MySpace, Friendster, Facebook, etc.), collaboration networks, and Protein-Protein Interaction (PPI), Gene Regulatory Networks (GRN) and Metabolic Networks (MN) in bioinformatics, are among several applications. Discovering communities in Networks is a recent and critical task in order to understand and model network structures. Several methods exist for community detection, such as modularity, clique, and random walk methods. These methods are somewhat limited because of the time needed to detect communities and their modularity. In this work, a Clique-based Community Detection Algorithm (CCDA) is proposed to overcome time and modularity limitations. The clique method is suitable since it arises in many real-world problems, as in bioinformatics, computational chemistry, and social networks. In definition, clique is a group of individuals who interact with one another and share similar interests. Based on this definition, if one vertex of a clique is assigned to a specific community, all other vertices in this clique often belong to the same community. CCDA develops a simple and fast method to detect maximum clique for specific vertex. In addition, testing is done for the closest neighbor node instead of testing all nodes in the graph. Since neighbor nodes are also sorted in descending order, it contributes to save more execution time. Furthermore, each node will be visited exactly once. To test the performance of CCDA, it is compared with previously proposed community detection algorithms (Louvain, and MACH with DDA-M2), using various datasets: Amazon (262111 nodes/1234877 vertices), DBLP (317080 nodes/1049866 vertices), and LiveJournal (4847571 nodes, 68993773 vertices). Results have proven the efficiency of the proposed method in terms of time performance and modularity.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Broder, A., Kumar, R., Maghoul, F., Raghavan, P., Rajagopalan, S., Stata, R., Tomkins, A., Wiener, J.: Graphstructure in the web. Comput. Netw. 33, 309–320 (2000)
Mahmoud, H., Masulli, F., Rovetta, S., Russo, G.: Community detection in protein-protein interaction networks using spectral and graph approaches. In: Computational Intelligence Methods for Bioinformatics and Biostatistics. Lecture Notes in Computer Science, vol. 8452. Springer, Cham (2014)
Rajaraman, A., Leskovec, J., Ullman, J.D.: Mining of Massive Datasets (2010)
Newman, M.E.J.: The structure of scientific collaboration networks. Proc. Natl. Acad. Sci. U.S.A. 98, 404–409 (2001)
Meyers, R.A. (ed.).: Encyclopedia of Complexity and Systems Science. Springer, Berlin (2009). 978-0-387-30440-3. Fortunato, S., Castellano, C.
Saha, B., Mandal, A., Tripathy, S.B., Mukherjee, D.: Complex networks, communities and clustering: a survey. CoRR, abs/1503.06277 (2015)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. EStatistical Nonlinear Soft Matter Phys. 69(2 Pt 2), 116 (2003)
Meyers, R.A. (ed.).: Encyclopedia of Complexity and Systems Science, vol. 1. Springer, Berlin, eprint arXiv:0712.2716 (2009). Fortunato, S., Castellano, C.
Bron, C., Kerbosch, J.: Finding all cliques of an undirected graph. Commun. ACM 16, 575–577 (1973)
Gao, W., Wong, K.F., Xia, Y., Xu, R.: Clique percolation method for finding naturally cohesive and overlapping document clusters. In: Matsumoto, Y., Sproat, R.W., Wong, K.F., Zhang, M. (eds.) Computer Processing of Oriental Languages. Beyond the Orient: The Research Challenges Ahead. ICCPOL 2006. Lecture Notes in Computer Science, vol. 4285. Springer, Berlin (2006)
Palsetia, D., Patwary, M.M.A., Hendrix, W., Agrawal, A., Choudhary, A.: Clique guided community detection. In: IEEE International Conference on Big Data (Big Data) (2014)
Blondel, V.D., Guillaume, J.-L.: Renaud Lambiotte and Etienne Lefebvre: Fast unfolding of communities in large networks. arXiv:0803.0476 (2008)
He, K., Li, Y., Soundarajan, S., Hopcroft, J.E.: Hidden Community Detection in Social Networks. eprint arXiv:1702.07462 (2017)
Zachary, W.W.: An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33, 452–473 (1977)
Leskovec, J., Adamic, L., Huberman, B.A.: The dynamics of viral marketing. ACM Trans. Web (ACM TWEB), 1(1) (2007)
Yang, J., Leskovec, J.: Defining and evaluating network communities based on ground-truth. In: ICDM (2012)
Backstrom, L., Huttenlocher, D., Kleinberg, J., Lan, X.: Group formation in large social networks: membership, growth, and evolution. In: KDD (2006)
Leskovec, J., Lang, K., Dasgupta, A., Mahoney, M.: Community structure in large networks: natural cluster sizes and the absence of large well-defined clusters. Int. Math. 6(1), 29–123 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Saad, H., Soliman, T.H.A., Rady, S. (2018). Developing an Efficient Clique-Based Algorithm for Community Detection in Large Graphs. In: Hassanien, A., Shaalan, K., Gaber, T., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017. AISI 2017. Advances in Intelligent Systems and Computing, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-319-64861-3_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-64861-3_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-64860-6
Online ISBN: 978-3-319-64861-3
eBook Packages: EngineeringEngineering (R0)