Multi-level Algorithms for Modularity Clustering
SEA '09 Proceedings of the 8th International Symposium on Experimental Algorithms
On finding graph clusterings with maximum modularity
WG'07 Proceedings of the 33rd international conference on Graph-theoretic concepts in computer science
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The connection patterns among individuals or objects in complex (social) networks possess rich information that can be useful for conducting effecient network analysis. In particular we consider the task of community detection in social networks. Nowadays social networking sites allow users to categorize their friends into different lists. Some of the examples being 'circles' on Google +, 'lists' on Twitter and Facebook. This information captures the reason of friendship between two users. In this paper we explore how this information can lead to detecting better community structures. We pose this task as a community detection problem where the algorithm does not have the information about the underlying edges. Experiments are conducted on 3 real world social networks - Google +, Twitter and Facebook. The experiment shows that results obtained are better than the results obtained by community detection over the original graph.