摘要
根据网络节点的局部拓扑信息,给出了节点与社团的相似度度量方法,提出了一种新的发现网络模糊社团结构的粒子群算法。该算法在迭代过程中依据节点对不同社团的相似度来不断调整粒子的位置向量,减少了搜索的盲目性,提高了搜索效率。对不同规模的计算机生成网络和真实网络进行测试,实验结果表明,该方法能有效、快速的给出网络的模糊社团结构。
An important problem of using evolutionary algorithm to discover community structure in complex networks is how to reduce the search space of network partitions for speeding up convergence.This paper presents an approach to similarity measurement between nodes and communities based on the local topology information of network nodes,and proposes a new particle swarm optimization algorithm to detect fuzzy communities of network.In the iterative process of algorithm the position vector of particle is modified according to similarity degrees between nodes and communities to promote search efficiency.Experiments on various scale computer-generated networks and real world networks show the capability and efficiency of the method to find the fuzzy community structure of network.
出处
《电子科技大学学报》
EI
CAS
CSCD
北大核心
2011年第1期73-79,共7页
Journal of University of Electronic Science and Technology of China
基金
四川省教育厅科研资助项目(2006B064)
关键词
复杂网络
相异性指数
模糊社团结构
局部拓扑信息
粒子群算法
complex network
dissimilarity Index
fuzzy community structure
local network topology information
particle swarm optimization
