Title :
gSkeletonClu: Density-Based Network Clustering via Structure-Connected Tree Division or Agglomeration
Author :
Sun, Heli ; Huang, Jianbin ; Han, Jiawei ; Deng, Hongbo ; Zhao, Peixiang ; Feng, BoQin
Author_Institution :
Dept. of Comput. Sci., Xi´´an Jiaotong Univ., Xi´´an, China
Abstract :
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 ε, 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 ε lie in the edge weights of the corresponding CCMST. By means of tree divisive or agglomerative clustering, our algorithm can find the optimal parameter ε and detect communities, hubs and outliers in large-scale undirected networks automatically without any user interaction. Extensive experiments on both real-world and synthetic networks demonstrate the superior performance of gSkeletonClu over the baseline methods.
Keywords :
complex networks; pattern clustering; social networking (online); trees (mathematics); agglomerative clustering; community detection; complex networks; core connected maximal spanning tree; density based network clustering; edge weights; gSkeletonClu; graph skeleton based clustering; hubs; large-scale undirected networks; optimal parameter; outliers; real-world networks; structure connected tree division; synthetic networks; Community Discovery; Density-based Network Clustering; Hubs and Outliers; Parameter Selection;
Conference_Titel :
Data Mining (ICDM), 2010 IEEE 10th International Conference on
Conference_Location :
Sydney, NSW
Print_ISBN :
978-1-4244-9131-5
Electronic_ISBN :
1550-4786
DOI :
10.1109/ICDM.2010.69