【摘 要】
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Community search plays an important role in complex network analysis.It aims to find a densely connected subgraph containing the query node in a graph.However,the most existing community search method
【机 构】
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School of Information,Liaoning University,Shenyang Liaoning,China,110036
【出 处】
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第六届中国计算机学会大数据学术会议
论文部分内容阅读
Community search plays an important role in complex network analysis.It aims to find a densely connected subgraph containing the query node in a graph.However,the most existing community search methods do not consider the influence of nodes and can not perfectly support the search in large graphs,making them have limitations in practical applications.In this paper,we introduce a community model called center-core community based on k-core decomposition,which can both capture the influence of nodes and guarantee the cohesiveness of community.Then we devise a center-core community graph index(CCG-Index),and online search algorithms(SingleQuery and MultiQuery)which support efficient search of center-core community in optimal time.Extensive experiments on four real-world large networks demonstrate the efficiency and effectiveness of our methods.
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