A new blockmodeling based hierarchical clustering algorithm for web social networks

Authors:
Shaojie Qiao;Tianrui Li;Hong Li;Jing Peng;Hongmei Chen
Affiliations:
School of Information Science and Technology, Southwest Jiaotong University, No. 111, Erhuanlu Beiyiduan, Chengdu, Sichuan 610031, China;School of Information Science and Technology, Southwest Jiaotong University, No. 111, Erhuanlu Beiyiduan, Chengdu, Sichuan 610031, China;School of Information Science and Technology, Southwest Jiaotong University, No. 111, Erhuanlu Beiyiduan, Chengdu, Sichuan 610031, China;Department of Science and Technology, Chengdu Municipal Public Security Bureau, No. 136, Wenwu Road, Chengdu, Sichuan 610017, China;School of Information Science and Technology, Southwest Jiaotong University, No. 111, Erhuanlu Beiyiduan, Chengdu, Sichuan 610031, China
Venue:
Engineering Applications of Artificial Intelligence
Year:
2012

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Abstract

Cluster analysis for web social networks becomes an important and challenging problem because of the rapid development of the Internet community like YouTube, Facebook and TravelBlog. To accurately partition web social networks, we propose a hierarchical clustering algorithm called HCUBE based on blockmodeling which is particularly suitable for clustering networks with complex link relations. HCUBE uses structural equivalence to compute the similarity among web pages and reduces a large and incoherent network into a set of smaller comprehensible subnetworks. HCUBE is actually a bottom-up agglomerative hierarchical clustering algorithm which uses the inter-connectivity and the closeness of clusters to group structurally equivalent pages in an effective fashion. In addition, we address the preliminaries of the proposed blockmodeling and the theoretical foundations of HCUBE clustering algorithm. In order to improve the efficiency of HCUBE, we optimize it by reducing its time complexity from O(|V|^2) to O(|V|^2/p), where p is a constant representing the number of initial partitions. Finally, we conduct experiments on real data and the results show that HCUBE is effective at partitioning web social networks compared to the Chameleon and k-means algorithms.