复杂网络交叠团模糊分析与信息挖掘

模块结构是复杂网络重要拓扑属性之一。多数模块聚类算法将网络分割为彼此无重叠、不关联的孤立团,同时很少有方法兼具模糊聚类与聚类后分析能力。针对复杂网络交叠团的聚类与模糊分析方法设计问题,给出一种新的模糊度量及相应的模糊聚类方法,并以新度量为基础,设计出两种挖掘网络模糊拓扑特征的新指标：团间连接紧密程度和模糊点对交叠团的连接贡献度,将其用于网络交叠模块拓扑结构宏观分析和团间关键点提取。实验结果表明,使用该聚类与分析方法不仅可以获得模糊团结构,而且能够揭示出新的网络特征,该方法为复杂网络聚类后分析提供了新的视角。

Fuzzy clustering and information mining in complex networks

Network community structure is one of the most important topological properties of complex networks. Most of the existing community detection methods divide the network into separated groups rather than into overlapping ones. Moreover, there is seldom a method which is capable of both clustering the network and analyzing the resulted overlapping communities. To solve this problem, this paper presented a novel fuzzy metric and a soft clustering algorithm. Based on the novel metric, two topological fuzzy metric, which include clique-clique closeness degree and inter-clique connecting contribution degree, were devised and applied in the topological macro analysis and the extraction of key nodes in the overlapping communities. Experimental results indicate that, as an attempt of analysis after clustering, the new indicators and mechanics can uncover new topology features hidden in the network.