Identification of Communities from Social Networks
Seema Rani , Monica Mehrotra
A social network is a social structure of people, related (directly or indirectly) to each other through a common relation or interest. Social network analysis (SNA) is the study of social networks to understand their structure and behavior. For studying structural and behavioral properties of these networks, communities are identified by grouping of individuals according to given context into subgroups. Community detection is very rich domain in social network analysis as it is useful in various domains like business, marketing, healthcare etc. Data analytic techniques such as data mining and predictive modeling are being used to gain new insights into social network analysis (SNA). This has the unique ability to play a new role in exploring the context and situations that lead to efficient and effective predictions. Identifying these social communities can bring benefit to understanding and predicting user’s behaviors. This paper is an attempt to study the various approaches for community detection (CD), application area of CD and evaluation of CD algorithm. It also presents the emerging and ongoing research towards improvement in existing CD algorithms in the area of social network analysis.
Community Detection, Evaluation of Identified Communities, Healthcare, Overlapping Community Detection, Social Network.
 A. Clauset, M.E.J. Newman & C. Moore. Finding Community Structure in very large networks [J]. Physical Review E, vol 69, 06613, 2004.
 T.A. Dang, E. Viennet. Community Detection based on structural and Attribute Similarities. The Sixth International Conference on Digital Society. 2012.
 M. Ester, H. Kriegel, J. Sander, and X. Xu. A Density Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96), pp.226-231, 1996.
 Aston, N. & Hu, W. Community Detection in Dynamic Social Networks. Scientific Research Communications and Network, 6, 124-136. http://dx.doi.org/10.4236/cn.2014.62015. 2014.
 M. Newman. Detecting Community Structure in Networks. The European Physical Journal B- Condensed Matter and Complex systems, Vol. 38(2), pp. 321330, 2004.
 Y. Wang. An Improved complex Network Community Detection Algorithm Based on K-Means. Advances in Intelligent and Soft Computing, Vol.160, pp. 243-248, 2012.
 J. G. Anderson. Evaluation in Health Informatics: Social Network Analysis. pp. 189-204, 2005.
 Fortunato, S. Community Detection in Graphs. Physics Reports, 486, 75-174. http://dx.doi.org/10.1016/j.physrep.2009.11.002. 2010
 Xu, X., Yuruk, N., Feng, Z. and Schweiger, T. SCAN: A Structural Clustering Algorithm for Networks. KDD’07. ACM, 824-833. http://dx.doi.org/10.1145/1281192.1281280. 2007.
 Freeman, L. C., Centrality in Social Networks I: Conceptual Clarification. Social Networks, 1, 215-239, 1979.
 S. Narayanan. The betweenness Centrality of Biological Networks. M.Sc thesis, Virginia Polytechnic Institute and State University, Virginia, 2005.
 D. Gomez, J. Figueira, and A. Eusebio. Modeling Centrality Measures in Social Network Analysis Using Bi-Criteria Network Flow Optimization Problems. European Journal of Operation Research, Vol 226(2), pp. 354-365, 2013.
 Lei Tang and Hoan Liu, Community Detection and Mining in Social Media, Morgan & Claypool Publishers.
[Seema Rani, Monica Mehrotra (2015), Identification of Communities from Social Networks, International Journal of Innovative Research in Computer Science & Technology (IJIRCST), Vol-3, Issue-3, Page No-130-133], (ISSN 2347 - 5552). www.ijircst.org
Department of Computer Science, Jamia Millia Islamia University, New Delhi, India,+011 9811476855, (e-mail: firstname.lastname@example.org).