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2010
IEEE

gSkeletonClu: Density-Based Network Clustering via Structure-Connected Tree Division or Agglomeration

8 years 2 months ago
gSkeletonClu: Density-Based Network Clustering via Structure-Connected Tree Division or Agglomeration
Community detection is an important task for mining the structure and function of complex networks. Many pervious approaches are difficult to detect communities with arbitrary size and shape, and are unable to identify hubs and outliers. A recently proposed network clustering algorithm, SCAN, is effective and can overcome this difficulty. However, it depends on a sensitive parameter: minimum similarity threshold , but provides no automated way to find it. In this paper, we propose a novel density-based network clustering algorithm, called gSkeletonClu (graph-skeleton based clustering). By projecting a network to its Core-Connected Maximal Spanning Tree (CCMST), the network clustering problem is converted to finding core-connected components in the CCMST. We discover that all possible values of the parameter lie in the edge weights of the corresponding CCMST. By means of tree divisive or agglomerative clustering, our algorithm can find the optimal parameter and detect communities, hub...
Heli Sun, Jianbin Huang, Jiawei Han, Hongbo Deng,
Added 03 Mar 2011
Updated 03 Mar 2011
Type Journal
Year 2010
Where ICDM
Authors Heli Sun, Jianbin Huang, Jiawei Han, Hongbo Deng, Peixiang Zhao, Boqin Feng
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