基于随机网络集成模型的广义网络社区挖掘算法

根据结点的属性和链接关系,现实世界中的复杂网络大多可分为同配网络和异配网络,社区结构在这两类网络中均普遍存在. 准确地挖掘出两种不同类型网络的社区结构具有重要的理论意义和广泛的应用领域.由于待处理的网络类型通常未知, 因而难以事先确定应当选择何种类型的网络社区挖掘算法才能获得有意义的社区结构. 针对该问题, 本文提出了广义网络社区概念,力图将同配和异配网络社区结构统一起来. 本文提出了随机网络集成模型, 进而提出了广义网络社区挖掘算法G-NCMA. 实验结果表明: 该算法能够在网络类型未知的前提下准确地挖掘出有意义的社区结构, 并能分析出所得社区的类型特征.

A Random Network Ensemble Model Based Generalized Network Community Mining Algorithm

According to the attributes of nodes and the linkages between them, most real-world complex networks could be assortative and disassortative. Community structures are ubiquitous in both types of networks. The ability to discovery meaningful community structures from both types of networks is fundamental for theoretical research and practical applications. Since the types of exploratory networks to be processed are usually unknown beforehand, it is difficult to determine what specific algorithms should be applied to them to obtain meaningful community structures. To address this issue, a novel concept of generalized network community is proposed in order to unify two concepts of assortative and disassortative communities. Based on a random network ensemble model, a generalized community mining algorithm, called G-NCMA, is proposed. Experimental results demonstrate that the G-NCMA algorithm is able to properly mine potential communities from explorative networks, as well as to determine their respective types.