SHRINK: a structural clustering algorithm for detecting hierarchical communities in networks

Community detection is an important task for mining the structure and function of complex networks. Generally, there are several different kinds of nodes in a network which are cluster nodes densely connected within communities, as well as some special nodes like hubs bridging multiple communities and outliers marginally connected with a community. In addition, it has been shown that there is a hierarchical structure in complex networks with communities embedded within other communities. Therefore, a good algorithm is desirable to be able to not only detect hierarchical communities, but also identify hubs and outliers. In this paper, we propose a parameter-free hierarchical network clustering algorithm SHRINK by combining the advantages of density-based clustering and modularity optimization methods. Based on the structural connectivity information, the proposed algorithm can effectively reveal the embedded hierarchical community structure with multiresolution in largescale weighted...

• Type: Journal
• Year: 2010
• Where: CIKM
• Authors: Jianbin Huang, Heli Sun, Jiawei Han, Hongbo Deng, Yizhou Sun, Yaguang Liu
• Keywords: Algorithms, CIKM 2010, Community Detection, Complex Networks, Information Technology