Abstract
The advances in social networks has led to the concentration of research on analyzing people’s behaviors in these networks. Accordingly, detecting communities and the interactions between their members is one of the most important issues addressed by these studies. After the proposition of new community detection methods in recent years, due to the extensive volume of the information generated in social networks and the increasing growth in the size of these networks, researchers became more interested in local, rather than global, detection methods. This paper proposes a heuristic approach to detecting communities by investigating local information. Comparing this method with state-of-the-art approaches, it is observed that the proposed approach outperforms the compared methods in detecting communities and their members and provides more accurate results.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Alfalahi K, Atif Y, Harous S (2013) Community detection in social networks through similarity virtual networks. In: Proceedings of the 2013 IEEE/ACM international conference on advances in social networks analysis and mining, pp 1116–1123
Biswas A, Biswas B (2015) Investigating community structure in perspective of ego network. Expert Syst Appl 42(20):6913–6934
Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech: Theory Exp 10
Chen J, Zaïane O, Goebel R (2009) Local community identification in social networks. In: International conference on advances in social network analysis and mining, 2009. ASONAM’09. IEEE, pp 237–242
Chen Q, Fang M (2012) An efficient algorithm for community detection in complex networks. In: The 6th workshop on social network mining and analysis
Cui W, Xiao Y, Wang H, Wang W (2014) Local search of communities in large graphs. In: Proceedings of the 2014 ACM SIGMOD international conference on management of data, pp 991– 1002
Fagnan J, Zaiane O, Barbosa D (2014) Using triads to identify local community structure in social networks. In: IEEE/ACM International conference on advances in social networks analysis and mining (ASONAM), 2014. IEEE, pp 108–112
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
Hlaoui A, Wang S (2004) A direct approach to graph clustering. Neural Networks and Computational Intelligence 4:158–163
Khorasgani, Rabbany R, Chen J, Zaïane OR (2010) Top leaders community detection approach in information networks. In: 4th SNA-KDD workshop on social network mining and analysis
Lancichinetti A, Fortunato S, Radicchi F (2008) Benchmark graphs for testing community detection algorithms. Phys Rev E 78(4):046110
Lancichinetti A, Radicchi F, Ramasco JJ, Fortunato S (2011) Finding statistically significant communities in networks. PloS one 6(4)
Lim KH, Datta A (2013) A seed-centric community detection algorithm based on an expanding ring search. In: Proceedings of the first australasian web conference, vol 144. Australian Computer Society, Inc, pp 21–25
Luo F, Wang JZ, Promislow E (2006) Exploring local community structures in large networks. In: Proceedings of the 2006 IEEE/WIC/ACM international conference on web intelligence. IEEE Computer Society, pp 233–239
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
Moradi F, Olovsson T, Tsigas P (2014) A local seed selection algorithm for overlapping community detection. Advances in Social Networks Analysis and Mining (ASONAM)
Newman ME (2003) The structure and function of complex networks. SIAM Rev 45(2):167–256
Newman ME (2012) Communities, modules and large-scale structure in networks. Nat Phys 8(1):25–31
Newman ME (2013) Spectral methods for community detection and graph partitioning. Phys Rev E 88(4)
Papadopoulos S, Kompatsiaris Y, Vakali A (2010) A graph-based clustering scheme for identifying related tags in folksonomies. In: Data warehousing and knowledge discovery. Springer, pp 65–76
Rattigan MJ, Maier M, Jensen D (2007) Graph clustering with network structure indices. In: Proceedings of the 24th international conference on machine learning. ACM, pp 783–790
Sales-Pardo Marta, Guimera R, Moreira AA, Amaral LA (2007) Extracting the hierarchical organization of complex systems. Proc Natl Acad Sci 104(39):15224–15229
Schaeffer SE (2007) Graph clustering. Computer Science Review 1(1). Elsevier
Shah D, Zaman T (2010) Community detection in networks: The leader-follower algorithm. arXiv:1011.0774
Takaffoli M, Rabbany R, Zaïane OR (2013) Incremental Local Community Identification in Dynamic Social Networks. In: Proceedings of the 2013 IEEE/ACM international conference on advances in social networks analysis and mining
Yakoubi Z, Kanawati R (2014) Licod: a leader-driven algorithm for community detection in complex networks. Vietnam Journal of Computer Science 1(4):241–256
Zachary WW (1977) An information flow model for conflict and fission in small groups. J Anthropol Res:452–473
Zhang T, Wu B (2012) A method for local community detection by finding core nodes. In: Proceedings of the 2012 international conference on advances in social networks analysis and mining
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Tabarzad, M.A., Hamzeh, A. A heuristic local community detection method (HLCD). Appl Intell 46, 62–78 (2017). https://doi.org/10.1007/s10489-016-0824-9
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-016-0824-9