A better strategy of discovering link-pattern based communities by classical clustering methods

Authors:
Chen-Yi Lin;Jia-Ling Koh;Arbee L. P. Chen
Affiliations:
Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan;Department of Computer Science and Information Engineering, National Taiwan Normal University, Taipei, Taiwan;Department of Computer Science, National Chengchi University, Taipei, Taiwan
Venue:
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Year:
2010

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Abstract

The definition of a community in social networks varies with applications. To generalize different types of communities, the concept of link-pattern based community was proposed in a previous study to group nodes into communities, where the nodes in a community have similar intra-community and inter-community interaction behaviors. In this paper, by defining centroid of a community, a distance function is provided to measure the similarity between the link pattern of a node and the centroid of a community. The problem of discovering link-pattern based communities is transformed into a data clustering problem on nodes for minimizing a given objective function. By extending the partitioning methods of cluster analysis, two algorithms named G-LPC and KM-LPC are proposed to solve the problem. The experiment results show that KM-LPC outperforms the previous work on the efficiency, the memory utilization, and the clustering result. Besides, G-LPC achieves the best result approaching the optimal solution.