电子学报2017，Vol.45Issue(9)：2077-2084,8.DOI:10.3969/j.issn.0372-2112.2017.09.004
基于图流在线非负矩阵分解的社团检测
Graph Streams Community Detection via Online Nonnegative Matrix Factorizations
摘要
Abstract
While the existing online community detection methods mosfly only deal with the nodes and edges from the increment part,which are difficult to effectively detect the dynamic changes in the community structure.Based on this,a new method for the detection of flow graphs based on online non negative matrix factorization (ONMF) is proposed.Firstly,our method put graph data into the cache as continuous streams to deal with.Then,our method iteratively updates the existing community belonging matrix real-time using online nonnegative matrix decomposition architecture and by means of the projected gradient descent theory.Lastly,through effective learning rate and cache strategy setting,our method ensures the convergence and rationality of graph stream processing.Experiments on real network data sets show that ONM has a higher community detection quality compared with the existing methods.关键词
在线/非负矩阵分解/图流/社团检测Key words
online/nonnegative matrix factorization/graph streams/community detection分类
信息技术与安全科学引用本文复制引用
常振超,陈鸿昶,王凯,卫红权,黄瑞阳..基于图流在线非负矩阵分解的社团检测[J].电子学报,2017,45(9):2077-2084,8.基金项目
国家自然科学基金创新群体(No.61521003) （No.61521003）
国家自然科学基金(No.61171108) （No.61171108）
国家973重点基础研究发展计划(No.2012CB315901,No.2012CB315905) （No.2012CB315901,No.2012CB315905）
国家科技支撑计划(No.2014BAH30B01) （No.2014BAH30B01）
