Author/Authors :
Zhou, Yong School of Computer Science and Technology - China University of Mining and Technology, China , Sun, Guibin School of Computer Science and Technology - China University of Mining and Technology, China , Xing,Yan School of Computer Science and Technology - China University of Mining and Technology, China , Zhou, Ranran School of Computer Science and Technology - China University of Mining and Technology, China , Wang, Zhixiao School of Computer Science and Technology - China University of Mining and Technology, China
Abstract :
in order to discover the structure of local community more effectively, this paper puts forward a new local community detectionalgorithm based on minimal cluster. Most of the local community detection algorithms begin from one node. The agglomerationability of a single node must be less than multiple nodes, so the beginning of the community extension of the algorithm in this paperis no longer from the initial node only but from a node cluster containing this initial node and nodes in the cluster are relativelydensely connected with each other. The algorithm mainly includes two phases. First it detects the minimal cluster and then finds thelocal community extended from the minimal cluster. Experimental results show that the quality of the local community detectedbyouralgorithmismuchbetterthanotheralgorithmsnomatterinrealnetworksorinsimulatednetworks.
