Abstract
With the rapid increase in popularity of online social networks, community detection in these networks has become a key aspect of research field. Overlapping community detection is an important NP-hard problem of social network analysis. Modularity-based community detection is one of the most widely used approaches for social network analysis. However, modularity-based community detection technique may fail to resolve small-size communities. Hence, we propose a novel two-step approach for overlapping community detection in social networks. In the first step, modularity density-based hybrid meta-heuristics approach is used to find the disjoint communities and the quality of these disjoint communities can be verified using Silhouette coefficient. In the second step, the quality disjoint communities with low computation cost are used to detect overlapping nodes based on Min-Max Ratio of minimum(indegree, outdegree) to the maximum(indegree, outdegree) values of nodes. We tested the proposed algorithm based on 10 standard community quality metrics along with Silhouette score using seven standard datasets. Experimental results demonstrate that the proposed approach outperforms the current state-of-the-art works in terms of quality and scalability.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Adamcsek B, Palla G, Farkas IJ, Dernyi I, Vicsek T (2006) CFinder: locating cliques and overlapping modules in biological networks. Bioinformatics 22(8):1021–1023
Adamic LA, Glance N (2005) The political blogosphere and the 2004 US election: divided they blog. In: Proceedings of the 3rd international workshop on Link discovery. ACM, pp 36–43. http://www-personal.umich.edu/~mejn/netdata/
Amiri B, Hossain L, Crawford JW (2011) An efficient multiobjective evolutionary algorithm for community detection in social networks. In: 2011 IEEE congress on Evolutionary Computation (CEC). IEEE, pp 2193–2199
Cao J, Bu Z, Gao G, Tao H (2016) Weighted modularity optimization for crisp and fuzzy community detection in large-scale networks. Phys A Stat Mech Appl 462:386–395
Chakraborty T, Srinivasan S, Ganguly N, Mukherjee A, Bhowmick S (2014) On the permanence of vertices in network communities. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 1396–1405
Chen M, Szymanski BK (2015) Fuzzy overlapping community quality metrics. Soc Netw Anal Min 5(1):1–14
Chen Y, Qiu X (2013) Detecting community structures in social networks with particle swarm optimization. In: Su J, Zhao B, Sun Z, Wang X, Wang F, Xu K (eds) Frontiers in internet technologies. Communications in computer and information science. Springer, Berlin, pp 266–275
Chu SC, Tsai PW, Pan JS (2006) Cat swarm optimization. In: Pacific Rim international conference on artificial intelligence. Springer, Berlin, pp 854–858
Dickinson B, Valyou B, Hu W (2013) A genetic algorithm for identifying overlapping communities in social networks using an optimized search space. Soc Netw 2:193–201
Fortunato S (2010) Community detection in graphs. Phys Rep 486(3):75–174
Girvan M, Newman ME (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99(12):7821–7826. http://www-personal.umich.edu/~mejn/netdata/
Gong M, Fu B, Jiao L, Du H (2011) Memetic algorithm for community detection in networks. Phys Rev E 84(5):056101
Gong M, Cai Q, Chen X, Ma L (2014) Complex network clustering by multiobjective discrete particle swarm optimization based on decomposition. IEEE Trans Evol Comput 18(1):82–97
Hafez AI, Ghali NI, Hassanien AE, Fahmy AA (2012) Genetic algorithms for community detection in social networks. In: 2012 12th international conference on intelligent systems design and applications (ISDA). IEEE, pp 460–465
Jami V, Guddeti RMR (2016) A hybrid community detection based on evolutionary algorithms in social networks. In: 2016 IEEE students’ conference on electrical, electronics and computer science (SCEECS). IEEE, pp 1–6
Kennedy J, Eberhart R(1995) Particle swarm optimization (PSO). In: Proceedings of the IEEE international conference on neural networks, Perth, Australia, pp 1942–1948
Lancichinetti A, Fortunato S (2009) Community detection algorithms: a comparative analysis. Phys Rev E 80(5):056117
Li Z, Zhang S, Wang RS, Zhang XS, Chen L (2008) Quantitative function for community detection. Phys Rev E 77(3):036109
Lin SW, Lee ZJ, Chen SC, Tseng TY (2008) Parameter determination of support vector machine and feature selection using simulated annealing approach. Appl Soft Comput 8(4):1505–1512
Lusseau D, Schneider K, Boisseau OJ, Haase P, Slooten E, Dawson SM (2003) The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations. Behav Ecol Sociobiol 54(4):396–405. http://www-personal.umich.edu/~mejn/netdata/
Meena J, Devi VS (2015) Overlapping community detection in social network using disjoint community detection. In: 2015 IEEE symposium series on computational intelligence. IEEE, pp 764–771
Mu CH, Xie J, Liu Y, Chen F, Liu Y, Jiao LC (2015) Memetic algorithm with simulated annealing strategy and tightness greedy optimization for community detection in networks. Appl Soft Comput 34:485–501
Newman ME (2006) Finding community structure in networks using the eigenvectors of matrices. Phys Rev E 74(3):036104
Newman ME, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69(2):026113
Pradhan PM, Panda G (2012) Solving multiobjective problems using cat swarm optimization. Expert Syst Appl 39(3):2956–2964
Santosa B, Ningrum MK (2009) Cat swarm optimization for clustering. In: International conference of soft computing and pattern recognition. SOCPAR’09. IEEE, pp 54–59
Su J, Havens TC (2014) Fuzzy community detection in social networks using a genetic algorithm. In: 2014 IEEE international conference on fuzzy systems (FUZZ-IEEE). IEEE, pp 2039–2046
Tasgin M, Herdagdelen A, Bingol H (2007) Community detection in complex networks using genetic algorithms. arXiv preprint arXiv:0711.0491
Watts DJ, Strogatz SH (1998) Collective dynamics of small-world networks. Nature 393(6684):440–442. http://www-personal.umich.edu/~mejn/netdata/
Xie J, Szymanski BK, Liu X (2011) Slpa: uncovering overlapping communities in social networks via a speaker-listener interaction dynamic process. In: 2011 IEEE 11th international conference on data mining workshops (ICDMW). IEEE, pp 344–349
Yang J, Leskovec J (2013) Overlapping community detection at scale: a nonnegative matrix factorization approach. In: Proceedings of the sixth ACM international conference on web search and data mining. ACM, pp 587–596
Zachary WW (1977) An information flow model for conflict and fission in small groups. J Anthropol Res 33(4):452–473. http://www-personal.umich.edu/mejn/netdata/
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Sarswat, A., Jami, V. & Guddeti, R.M.R. A novel two-step approach for overlapping community detection in social networks. Soc. Netw. Anal. Min. 7, 47 (2017). https://doi.org/10.1007/s13278-017-0469-7
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s13278-017-0469-7