摘要
针对加权网络的社团结构划分问题,提出了一种基于节点相似度的划分算法.构造一种新型加权网络的节点相似度矩阵,基于该相似度矩阵,随机选取一个节点作为初始社团,搜索与该节点相似度值最大的节点合并成一个新的社团.反复迭代,形成划分.该算法具有较低的计算复杂度.用经典复杂网络的社团划分算例验证了该算法的有效性.
Based on node similarity, a method for detecting community structure in a weighted network is proposed. A novel node similarity matrix of the weighted network is constructed, and then an arbitrary node is chosen as initial node based on it. A node having maximum similarity to the initial node is searched, and the two nodes are merged into a new community. The community structure is discovered iteratively, and the partition is formed eventually. The presented method has low computational complexity. Furthermore, the effectiveness of the algorithm is validated by the numerical examples of community detection with classic complex network.
出处
《信息与控制》
CSCD
北大核心
2012年第4期504-508,共5页
Information and Control
基金
国家自然科学基金资助项目(71071079)
关键词
加权网络
社区结构
共同邻居
节点相似度
weighted network
community structure
common neighbor
node similarity
