Improved Modularity Based on Girvan-Newman Modularity
Social networks can be modeled by graphs with nodes and edges, and communities are sub graphs within networks — groups of nodes within which connections are dense, but between them connections are sparser. According to this property of communities, this paper proposes a new modularity for measuring how good a particular division is based on the concept of coupling coefficient. Further more, this paper applies proposed modularity to synthetic network data and compares the computational results under different modularity. The experimental results show that our new modularity is suitable for the cases that all communities have nearly the same number of links, and it is also suitable for the cases that the number of links in a community differs greatly from the one in another community.
Social Network Community Detection Modularity
Kong Bing Zhou Lihua Liu Weiyi
Department of Computer Science and Engineering, School of Information, Yunnan University, Kunming 650091, China
国际会议
三亚
英文
293-296
2012-01-06（万方平台首次上网日期，不代表论文的发表时间）