基于社团强度系数的社团结构发现算法

社团结构是复杂网络普遍存在的拓扑特性之一.为了将复杂网络中的社团结构有效地划分出来,在对强社团定义的基础上,引入社团强度系数的概念,提出了一种基于社团强度系数的社团结构发现算法.该算法具有较低的时间复杂度,通过不断寻找网络最大度数的节点及其邻居节点,计算其社团强度系数来衡量社团如何划分.主要针对Zachary网络和Dolphin网络等进行了仿真实验,结果表明该算法具有较高的社团划分准确度、较好的敏感性和良好的可扩展性,充分验证了其可行性和有效性.

Community Structure Detection Algorithm Based on Community Strength Coefficient

Community structure is one of the ubiquitous topology characteristics of complex network.In order to divide the community structure effectively in complex networks,this paper introduced the concept of community strength coefficient based on the definition of community strength,and put forward a kind of community structure detection algorithm based on community strength coefficient.The algorithm has a lower time complexity,and it looks for a network node that has maximum degree of intensity coefficient and its neighbor nodes to calculate the community strength coefficient and measure how to divide the community.The simulated experiment was mainly made based on Zachary network and Dolphin network to verify the feasibility and effectiveness.The algorithm has higher accuracy,better sensitivity and better extensibility to divide community.