复杂网络聚类方法
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Supported by the National Natural Science Foundation of China under Grant Nos.60496321, 60503016, 60573073, 60873149 (国家自然科学基金); the National High-Tech Research and Development Plan of China under Grant No.2006AA10Z245 (国家高技术研究发展计划(863)


Complex Network Clustering Algorithms
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    摘要:

    网络簇结构是复杂网络最普遍和最重要的拓扑属性之一,具有同簇节点相互连接密集、异簇节点相互连接稀疏的特点.揭示网络簇结构的复杂网络聚类方法对分析复杂网络拓扑结构、理解其功能、发现其隐含模式、预测其行为都具有十分重要的理论意义,在社会网、生物网和万维网中具有广泛应用.综述了复杂网络聚类方法的研究背景、研究意义、国内外研究现状以及目前所面临的主要问题,试图为这个新兴的研究方向勾画出一个较为全面和清晰的概貌,为复杂网络分析、数据挖掘、智能Web、生物信息学等相关领域的研究者提供有益的参考.

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    Network community structure is one of the most fundamental and important topological properties of complex networks, within which the links between nodes are very dense, but between which they are quite sparse. Network clustering algorithms which aim to discover all natural network communities from given complex networks are fundamentally important for both theoretical researches and practical applications, and can be used to analyze the topological structures, understand the functions, recognize the hidden patterns, and predict the behaviors of complex networks including social networks, biological networks, World Wide Webs and so on. This paper reviews the background, the motivation, the state of arts as well as the main issues of existing works related to discovering network communities, and tries to draw a comprehensive and clear outline for this new and active research area. This work is hopefully beneficial to the researchers from the communities of complex network analysis, data mining, intelligent Web and bioinformatics.

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杨 博,刘大有,LIU Jiming,金 弟,马海宾.复杂网络聚类方法.软件学报,2009,20(1):54-66

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  • 收稿日期:2008-06-17
  • 最后修改日期:2008-08-28
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