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Communities in Complex Networks: A Review

Anand Mishra

Abstract


These days we are equipped with various types of networks like social networks, biological networks, technological networks etc. These networks exist almost everywhere. Many scientists and researchers have shown their interest in these complex networks because of their huge range of applications. These complex networks have various types of properties like transitivity, scale free networks, presence of community structure, etc. Community detection is one of the most active fields in complex networks because it has many practical applications. In the paper report, we have discussed all the work done till date in the field of community detection.

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C. Gkantsidis, M. Mihail, E. Zegura. Spectral analysis of internet topologies, IEEE INFOCOM. 2003. [2] P. Boldi, S. Vigna. “The webgraph framework i: compression techniques,” in WWW, 2004. [3] S. Voulgaris, A.-M. Kermarrec, L. Massouli´e, M. van Steen. Exploiting semantic proximity in

peer-to-peer content searching, IEEE FTDCS. 2004. [4] R. Albert, A. Barab_asi. Statistical mechanics of complex networks, Rev Modern Phys. 2002; 74(1): 47– 97p. [5] R. Pastor-Satorras, A. Vespignani. Epidemic spreading in scale-free networks, Phys Rev Lett. 2001; 86(14): 32003203p. [6] Z. Li, P. Mohapatra. Impact of topology on overlay routing service, IEEE INFOCOM. 2004. [7] S. Fortunato. Community detection in graphs, Phys Rep. 2010; 486(35): 75–174p. [8] [8] J. Richardt, S. Bornholdt. Statistical mechanics of community detection, Phys Rev 2006; E74: 016110p. [9] N. Gulbahce, S. Lehmann. The art of community detection, Bioessays. 2008; 30(10): 934–8p. [10] K.-Il Goh, M.E. Cusick, D. Valle, B. Childs, M. Vidal, et al. The human disease network, Proc Natl Acad Sci USA. 2007. [11] F. Radicchi, C. Castellano, F. Cecconi, V. Loreto, D. Parisi. Defining and identifying communities in networks, PNAS USA. 2004; 101(9): 2658–63p. [12] M.E. Newman, M. Girvan. Finding and evaluating community structure in networks, Phys Rev E. 2004; 69(2). [13] A. Clauset, M.E.J. Newman, C. Moore. Finding community structure in very large networks, Phys Rev E. 2004; 1–6p. [14] A. Pothen. Graph partitioning algorithms with applications to scientific computing, Technical Report. Norfolk, VA, USA, 1997. [15] M. Girvan, M.E.J. Newman. Community structure in social and biological networks, Proc Natl Acad Sci USA. 2002; 99(12): 7821–6p. [16] P. Pons, M. Latapy. Computing communities in large networks using random walks, arXiv, physics/0512106, (2005). [17] H. Balakrishnan, N. Deo. Discovering communities in complex networks, In: Proc of 44th ACMSE. 280–5p. [18] K. Wakita, T. Tsurumi. Finding community structure in mega-scale social networks: [extended abstract]. In: Proceedings of the 16th International Conference on World Wide Web. WWW ’07, ACM, New York, NY, USA, 2007, 1275–6p. [19] U.N. Raghavan, R. Albert, S. Kumara. Near linear time algorithm to detect community structures in large-scale networks, Phys Rev E. 2007; 76: 036106p. [20] V.D. Blondel, J.L. Guillaume, R. Lambiotte, E. Lefebvre. Fast unfolding of communities in large networks, J Stat Mech – Theor Exp. 2008. [21] M. Rosvall, C.T. Bergstrom. An information-theoretic framework for resolving community structure in complex networks, In: Proceeding in National Academy of Sciences. 2007; 104(18): 7327–31p. [22] C. Pizzuti. GA-Net: a genetic algorithm for community detection in social networks, parallel problem solving from nature – PPSN X, Lecture Notes in Computer Science. 2008; 5199: 1081–90p. [23] J. Huang, H. Sun, J. Han, H. Deng, Y Sun, Y. Liu. SHRINK: a structural clustering algorithm for detecting hierarchical communities in networks, In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management. 2010; 219–28p. [24] A. Lancichinetti, S. Fortunato. Community detection algorithms: a comparative analysis. 2010. [25] P. De Meo, E. Ferrara, G. Fiumara, A. Provetti. Generalized louvain method for community detection in

large networks, In: Intelligent Systems Design and Applications (ISDA), 2011 11th International Conference. 2011, 88–93p. [26] J. Yang, J. McAuley, J. Leskovec. Community detection in networks with node attributes, arXiv preprint arXiv:1401.7267, 2014. [27] R. Agrawal. Bi-objective community detection (bocd) in networks using genetic algorithm, In: Contemporary Computing. Springer Berlin Heidelberg; 2011, 5–15p. [28] Y. Cai, C. Shi, Y. Dong, Q. Ke, B. Wu. A novel genetic algorithm for overlapping community detection, In: Advanced Data Mining and Applications. Springer Berlin Heidelberg; 2011; 97–108p. [29] J. Xie, S. Kelley, B.K. Szymanski. Overlapping community detection in networks: The state-of-the-art and comparative study, ACM Computing Surveys (CSUR). 2013. [30] A. Lancichinetti, S. Fortunato, F. Radicchi. Benchmark graphs for testing community detection algorithms, Phys Rev E. 2008; 78. [31] T. Ma, Y. Wang, M. Tang, J. Cao, Y. Tian, A. Al-Dhelaan, M. AlRodhaan, LED: A fast overlapping communities detection algorithm based on structural clustering, Neurocomputing. 2016. [32] Z. Li, J. Liu. A multi-agent genetic algorithm for community detection in complex networks, Phys A: Stat Mech Appl. 2016; 449: 336–47p. [33] A. Mahmood, M. Small. Subspace based network community detection using sparse linear coding, IEEE Trans Knowl Data Eng. 2016; 28(3): 801–12p. [34] J. Whang, D. Gleich, I. Dhillon. Overlapping community detection using neighborhood-inflated seed expansion, IEEE Trans Knowl Data Eng. 2016; 28(5): 1272–84p. 35] F. Zhang, J. Li, F. Li, M. Xu, R. Xu, X. He. Community detection based on links and node features in social

networks, Multimedia Model. 2015; 418–29p.




DOI: https://doi.org/10.37628/ijoippr.v3i1.271

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