社团结构作为复杂网络的拓扑特性之一具有重要的理论和实践意义.提出一种基于节点依赖度和相似社团融合的社团结构发现算法,首先根据依赖度和相似度的定义将整个网络划分成若干个平均集聚系数较大的局部网络,构成网络的基础骨架社团;然后根据连接度的定义不断将社团边缘的节点和小社团吸收到相应的骨架网络中去,直到所有节点都得到准确的社团划分.算法在Zachary空手道俱乐部网络和海豚社会网络中进行了社团划分实验,并与GN算法和Newman快速算法进行了比较,结果表明该算法可以有效地划分社团边缘的模糊节点,社团划分结果具有较高的准确度.%As one of the topological properties of complex networks,the community structure has important theoretical and practical significance.We propose a community detection algorithm based on node dependence and the fusion of similar communities.The algorithm firstly divides the whole network into several local networks with large average clustering coefficients,thus constructing a skeleton of the complex networks.Then according to the definition of connectivity,the algorithm continuously absorbs the edge nodes of the community and small communities into the backbone network until all the nodes are accurately allocated to the community.This algorithm is applied to Zachary Karate Club network and the dolphin social network,and compared with the Girvan-Newman algorithm (GN) and Newman fast algorithm (NFA).The results show that our algorithm can effectively classify fuzzy edge nodes and the result of the community division has high accuracy.
展开▼
