互联网宏观拓扑的社团发现

Community Detecting of Internet Macroscopic Topology

自然界中存在的大量复杂系统都可以通过复杂网络加以描述,社团结构是继小世界特性和无标度特性之后发现的最为重要的复杂网络特性。社团发现对理解互联网的宏观拓扑结构至关重要。针对互联网宏观拓扑的结构特性,基于边聚簇算法思想,设计了一个基于路由特征的社团发现算法,以互联网宏观拓扑中的探测边频为影响因子定义边相似性,改造边聚簇算法中的关键聚簇过程,以发现互联网宏观拓扑中的社团结构。实验结果表明,所提算法与原算法相比,具有更高的分割密度。进一步以边介数替代探测边频,将该算法应用在其它类型网络中,同样取得了较好的效果。

A large number of complex systems in nature can be described by complex networks.Community structure is the most important feature of complex networks following the small-world and scale-free features.Community detecting is very important for understanding the macroscopic topology structure of Internet.Aimed at the structure features of the macroscopic topology of Internet,based on link clustering method,we proposed a community detecting algorithm which redefines link similarity with routing features to transform the link clustering process.It gives a better community structure in Internet macroscopic topology.It is further applied into other networks of different types by using link betweenness instead of link frequency,and better community structure can be gotten.