A graph clustering method for community detection in complex networks

作者：Zhou, HongFang^*; Li, Jin; Li, JunHuai; Zhang, FaCun; Cui, YingAn

来源：Physica A: Statistical Mechanics and Its Applications , 2017, 469: 551-562.

DOI：10.1016/j.physa.2016.11.015

• Complex networks
• Collaborative similarity
• Graph clustering
• Community detection

Information mining from complex networks by identifying communities is an important problem in a number of research fields, including the social sciences, biology, physics and medicine. First, two concepts are introduced, Attracting Degree and Recommending Degree. Second, a graph clustering method, referred to as AR-Cluster, is presented for detecting community structures in complex networks. Third, a novel collaborative similarity measure is adopted to calculate node similarities. In the AR-Cluster method, vertices are grouped together based on calculated similarity under a-K-Medoids framework. Extensive experimental results on two real datasets show the effectiveness of AR-Cluster.

• 出版日期2017-3-1
• 单位西安理工大学