复杂网络子图中心性分析

Analysis of Subgraph Centrality in Complex Networks

许多现实网络虽然具有小世界和无标度的全局结构特征,但可能具有非常不同的局部结构特征,子图从局部层次刻画了网络内部相互连接的特定模式.本文通过仿真分析的方法研究了ER随机网络、WS小世界网络和BA无标度网络模型的子图中心性的特点,得出了这三类网络模型的子图中心性与网络密度、重连概率和网络规模之间的关系.仿真结果表明,子图中心性能够刻画复杂网络的拓扑结构.

Many real networks have been shown to share global statistical features, such as the ＂small world＂ and the ＂scale-free＂ effects, but they have very different local structural properties. Subgraphs characterize specific patterns of interconnection at the local level. In this paper, characteristics of subgraph centrality are investigated through theory-guided extensive numerical simulations for the ER random network, the WS small-world network and the BA scale-free network, respectively. It was presented that the relationship between the subgraph centrality and density, rewiring probability and the network size for three complex network models. Experimental results show the subgraph centrality can characterizes true topological structure of the complex networks.