Uncovering the effect of dominant attributes on community topology: A case of facebook networks
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Community structure points to structural patterns and reflects organizational or functional associations of networks. In real networks, each node usually contains multiple attributes representing the node’s characteristics. It is difficult to identify the dominant attributes, which have definitive effects on community formation. In this paper, we obtain the overlapping communities using game-theoretic clustering and focus on identifying the dominant attributes in terms of each community. We uncover the association of attributes to the community topology by defining dominance ratio and applying Pearson correlation. We test our method on Facebook data of 100 universities and colleges in the U.S. The study enables an integrating observation on how the offline lives infer online consequences. The results showed that people in class year 2010 and people studying in the same major tend to form denser and smaller groups on Facebook. Such information helps e-marketing campaigns target right customers based on demographic information and without the knowledge of underlying social networks.
KeywordsDominant attribute Community detection Facebook Game-theoretic clustering Dominance ratio Community topology
- Baumes, J., Goldberg, M. K., Krishnamoorthy, M. S., Magdon-Ismail, M., & Preston, N. (2005). Finding communities by clustering a graph into overlapping subgraphs. IADIS AC, 5, 97–104.Google Scholar
- Bonneau, J., Anderson, J., Anderson, R., & Stajano, F. (2009). Eight friends are enough: social graph approximation via public listings. In Proceedings of the Second ACM EuroSys Workshop on Social Network Systems (pp. 13–18). ACM.Google Scholar
- Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A k-means clustering algorithm. Applied Statistics, 100–108.Google Scholar
- Mandala, S., Kumara, S., & Chatterjee, K. (2014). A Game-Theoretic Approach to Graph Clustering. INFORMS Journal on Computing.Google Scholar
- Mislove, A., Viswanath, B., Gummadi, K. P., & Druschel, P. (2010). You are who you know: inferring user profiles in online social networks. In Proceedings of the third ACM international conference on Web search and data mining (pp. 251–260). ACM.Google Scholar
- Porter, M. A. (2011). Facebook 100 Data Set in Quantum Chaotic Thoughts. Retrieved from http://masonporter.blogspot.com/2011/02/facebook100-data-set.html. Accessed May 2011.
- Pujol, J. M., Erramilli, V., & Rodriguez, P. (2009). Divide and conquer: Partitioning online social networks. arXiv preprint arXiv:0905.4918.Google Scholar
- Sarmanov, O. V. (1962). Maximum correlation coefficient (nonsymmetric case). Selected Translations in Mathematical Statistics and Probability, 2, 207–210.Google Scholar
- Xie, J., Szymanski, B. K., & Liu, X. (2011). Slpa: Uncovering overlapping communities in social networks via a speaker-listener interaction dynamic process. In Data Mining Workshops (ICDMW), 2011 I.E. 11th International Conference on (pp. 344–349). IEEE.Google Scholar
- Xu, X., Yuruk, N., Feng, Z., & Schweiger, T. A. (2007). Scan: a structural clustering algorithm for networks. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 824–833). ACM.Google Scholar