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    复杂网络社区挖掘综述

    Community Mining in Complex Networks

    • 摘要: 复杂网络社区挖掘是近10年来多学科交叉的前沿研究热点之一,其研究不仅有重要的理论意义,而且有广泛的应用前景.介绍了社区挖掘及重叠社区挖掘的研究背景和研究意义,分析了研究现状,讨论了该研究所面临的一些主要问题及未来的发展方向.同时,为了对不同的社区挖掘算法进行更好地评估,选择了有代表性的6个社区挖掘算法和3个重叠社区挖掘算法进行测试,并给出了对比分析结果,试图为这个新兴研究领域勾画出一个较为全面和清晰的轮廓.

       

      Abstract: Many complex systems in the real world exist in the form of networks, such as social networks, biological networks, Web networks, etc, which are collectively named complex networks. The research of complex networks has attracted many researchers from different fields such as physics, mathematics, computer science, among others. One of the main problems in the study of complex network is the detection of community structure, i.e. the division of a network into groups of nodes with dense intra-connections and sparse inter-connections, which has recently triggered great interest. The ability to detect community structure has a large amount of usefulness in many aspects. Furthermore, community structure may provide insights in understanding some uncharacteristic properties of a complex network system. For instance, in the World Wide Web, community analysis has uncovered thematic clusters; in biochemical or neural networks, communities may be functional groups and separating the network into such groups could simplify functional analysis considerably. This paper reviews the background, the significance and the state-of-the-art in discovering (overlapping) communities. Also, it raises several open issues as the conclusion. Here we try to draw a comprehensive overview for this emerging scientific area, with the purpose of offering some beneficial suggestions for related researchers.

       

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