Abstract
Community detection in social networks plays a vital role. The understanding and detection of communities in social networks is a challenging research problem. There exist many methods for detecting communities in large scale networks. Most of these methods presume the predefined number of communities and apply detection methods to exactly find out the predefined number of communities. However, there may not be the predefined number of communities naturally occurring in the social networks. Application of brute force inorder to predefine the number of communities goes against the natural occurrence of communities in the networks. In this paper, we propose a method for community detection which explores Self Organizing Maps for natural cluster selection and modularity measure for community strength identification. Experimental results on the real world network datasets show the effectiveness of the proposed approach.
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
Similar content being viewed by others
References
Watts, D.J., Strogatz, S.H.: Collective dynamics of small-world networks. Nature 393, 440–442 (1998)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004)
Ting, I.H.: Web mining techniques for online social networks analysis. In: Proceedings of International Conference on Service Systems and Service Management, pp. 1–5. Melbourne, June 30–July 2 (2008)
Hopcroft, J.E., Khan, O., Kulis, B., Selman, B.: Natural communities in large linked networks. In: Proceedings of International Conference on Knowledge Discovery and Data Mining, pp. 541–546 (2003)
Newman, M.E.J.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577–8582 (2006)
Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Phys. Rev. E 69, 066133 (2004)
Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70, 066111 (2004)
White, S., Smyth, P.: A spectral clustering approach to finding communities in graph. In: SDM (2005)
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99(12), 7821–7826 (2002)
Li, Z., Wang, R., Zhang, X.-S., Chen, L.: Self organizing map of complex networks for community detection. J. Syst. Sci. Complexity. 23(5), 931–941 Springer-Verlag (2010)
Zachary, W.W.: An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33(4), 452–473 (1977)
Lusseau, D.: The emergent properties of a dolphin social network. Proc. R. Soc. B: Biol. Sci. 270(2), S186–S188 (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer India
About this paper
Cite this paper
Enugala, R., Rajamani, L., Ali, K., Kurapati, S. (2016). Identifying Natural Communities in Social Networks Using Modularity Coupled with Self Organizing Maps. In: Behera, H., Mohapatra, D. (eds) Computational Intelligence in Data Mining—Volume 1. Advances in Intelligent Systems and Computing, vol 410. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2734-2_37
Download citation
DOI: https://doi.org/10.1007/978-81-322-2734-2_37
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2732-8
Online ISBN: 978-81-322-2734-2
eBook Packages: EngineeringEngineering (R0)