Share This Article:

A Combination Approach to Community Detection in Social Networks by Utilizing Structural and Attribute Data

Abstract Full-Text HTML XML Download Download as PDF (Size:212KB) PP. 11-15
DOI: 10.4236/sn.2016.51002    3,040 Downloads   3,505 Views   Citations
Author(s)    Leave a comment

ABSTRACT

Community detection is one of the important tasks of social network analysis. It has significant practical importance for achieving cost-effective solutions for problems in the area of search engine optimization, spam detection, viral marketing, counter-terrorism, epidemic modeling, etc. In recent years, there has been an exponential growth of online social platforms such as Twitter, Facebook, Google+, Pinterest and Tumblr, as people can easily connect to each other in the Internet era overcoming geographical barriers. This has brought about new forms of social interaction, dialogue, exchange and collaboration across diverse social networks of unprecedented scales. At the same time, it presents new challenges and demands more effective, as well as scalable, graphmining techniques because the extraction of novel and useful knowledge from massive amount of graph data holds the key to the analysis of social networks in a much larger scale. In this research paper, the problem to find communities within social networks is considered. Existing community detection techniques utilize the topological structure of the social network, but a proper combination of the available attribute data, which represents the properties of the participants or actors, and the structure data of the social network graph is promising for the detection of more accurate and meaningful communities.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Muslim, N. (2016) A Combination Approach to Community Detection in Social Networks by Utilizing Structural and Attribute Data. Social Networking, 5, 11-15. doi: 10.4236/sn.2016.51002.

References

[1] Cook, D.J. and Holder, L.B. (2007) Mining Graph Data. John Wiley & Sons, Inc., Hoboken.
[2] McPherson, M., Smith-Lovin, L. and Cook, J.M. (2001) Birds of a Feather: Homophily in Social Networks. Annual Review of Sociology, 27, 415-444. http://dx.doi.org/10.1146/annurev.soc.27.1.415
[3] Kaufman, L. and Rousseeuw, P.J. (2005) Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley, Hoboken. http://dx.doi.org/10.1002/9780470316801.ch1
[4] Shi, J. and Malik, J. (2000) Normalized Cuts and Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 888-905. http://dx.doi.org/10.1109/34.868688
[5] Newman, M. and Girvan, M. (2004) Finding and Evaluating Community Structure in Networks. Physical Review E, 69, 1-16. http://dx.doi.org/10.1103/PhysRevE.69.026113
[6] Xu, X., Yuruk, N., Feng, Z. and Schweiger, T.A.J. (2007) Scan: A Structural Clustering Algorithm for Networks. International Conference on Knowledge Discovery and Data Mining (KDD’07), San Jose, 824-833. http://dx.doi.org/10.1145/1281192.1281280
[7] Combe, D., Largeron, C., Egyed-Zsigmond, E. and Gery, M. (2012) Combining Relations and Text in Scientific Network Clustering. 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Istanbul, 26-29 August 2012, 1248-1253.
http://dx.doi.org/10.1109/ASONAM.2012.215
[8] Zhou, Y., Cheng, H. and Yu, J. (2009) Graph Clustering Based on Structural/Attribute Similarities. Proceedings of the VLDB Endowment, 2, 718-729. http://dx.doi.org/10.14778/1687627.1687709
[9] Coscia, M., Giannotti, F. and Pedreschi, D. (2011) A Classification for Community Discovery Methods in Complex Networks. Statistical Analysis and Data Mining Journal. http://dx.doi.org/10.1002/sam.10133
[10] Leskovec, J., Lang, K.J., Dasgupta, A. and Mahoney, M.W. (2008) Statistical Properties of Community Structure in Large Social and Information Networks. Proceedings of 17th International Conference on World Wide Web, WWW’08, 695-704. http://dx.doi.org/10.1145/1367497.1367591
[11] Aggarwal, C.C. and Wang, H.X. (2010) Managing and Mining Graph Data. Springer, New York.
[12] Steinhaeuser, K. and Chawla, N. (2008) Community Detection in a Large Real-World Social Network. Social Computing, Behavioral Modeling, and Prediction, 168-175. http://dx.doi.org/10.1007/978-0-387-77672-9_19
[13] Gong, N.Z.Q., Xu, W.C., Huang, L., Mittal, P., Stefanov, E., Sekar, V. and Song, D. (2012) Evolution of Social-Attribute Networks: Measurements, Modeling, and Implications Using Google+. Internet Measurement Conference, 131-144. http://dx.doi.org/10.1145/2398776.2398792
[14] Mislove, A., Marcon, M., Gummadi, K.P. and Bhattacharjee, B. (2007) Measurement and Analysis of Online Social Networks. Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, 29-42. http://dx.doi.org/10.1145/1298306.1298311
[15] Kossinets, G. and Watts, D. (2006) Empirical Analysis of an Evolving Social Network. Science, 311, 88-90. http://dx.doi.org/10.1126/science.1116869
[16] Elhadi, H. and Agam, G. (2013) Structure and Attributes Community Detection: Comparative Analysis of Composite, Ensemble and Selection Methods. The 7th SNA-KDD Workshop’13 (SNA-KDD’13).
[17] Gunnemann, S., Farber, I., Boden, B. and Seidl, T. (2010) Subspace Clustering Meets Dense Subgraph Mining: A Synthesis of Two Paradigms. Proceedings of the IEEE International Conference on Data Mining, Sydney, 13-17 December 2010, 845-850. http://dx.doi.org/10.1109/ICDM.2010.95
[18] Lancichinetti, A. and Fortunato, S. (2012) Consensus Clustering in Complex Networks. Scientific Reports, 2, 336. http://dx.doi.org/10.1038/srep00336

  
comments powered by Disqus

Copyright © 2019 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.