通信学报2018，Vol.39Issue(4)：13-20,8.DOI:10.11959/j.issn.1000-436x.2018052
并行社区发现算法的可扩展性研究
Research on the scalability of parallel community detection algorithms
摘要
Abstract
The social network often contains a large amount of information about users and groups, such as topic evolu-tion mode, group aggregation effect, the law of information dissemination and so on. The mining of these information has become an important task for social network analysis. As one characteristic of the social network, the group aggregation effect is characterized by the community structure of the social network. The discovery of community structure has be-come the basis and key point of other social network analysis tasks. With the rapid growth of the number of online social network users, the traditional community detection methods have been difficult to be used, which contributes to the de-velopment of parallel community detection technology. The current mainstream parallel community detection methods, including Louvain algorithm and label propagation algorithm, were tested in the large-scale data sets, and corresponding advantages and disadvantages were pointed out so as to provide useful information for later applications.关键词
社区发现/并行算法/可扩展性Key words
community detection/parallel algorithm/scalability分类
信息技术与安全科学引用本文复制引用
刘强,贾焰,方滨兴,周斌,胡玥,黄九鸣..并行社区发现算法的可扩展性研究[J].通信学报,2018,39(4):13-20,8.基金项目
国家重点研发计划基金资助项目(No.2017YFB0803303) （No.2017YFB0803303）
国家自然科学基金资助项目(No.61502517, No.61472438)The National Key Research and Development Program of China (No. 2017YFB0803303), The National Natural Science Foundation of China (No.61502517, No.61472438) （No.61502517, No.61472438）
