电子学报2016，Vol.44Issue(3)：587-594,8.DOI:10.3969/j.issn.0372-2112.2016.03.014
基于隶属度的社会化网络重叠社区发现及动态集群演化分析
Overlapping Community Detection and Dynamic Group Evolution Analysis Based on the Degree of Membership in Social Network
摘要
Abstract
The complex social attributes of nodes in the network have a certain ability to maintain the former state,so it is inappropriate to determine community division merely based on newly added data.This paper proposes an overlapping community detection algorithm and dynamic cluster update strategy, which, by fully analyzing historical network data to compute the degree of nodes belonging to communities,determines the evolution tendency of nodes through incorporating in-cremental data to analyze the structure of the network and update the division results automatically.Experiments on several typical datasets demonstrate that the algorithm not only ensures the sensitivity to incremental data,but also avoids the nega-tive effect of temporary features in maintaining intrinsic states on the clustering results.关键词
社区发现/社会化网络/聚类/重叠社区/自适应算法Key words
community detection/social internet/clustering/overlapping community/adaptive algorithm分类
信息技术与安全科学引用本文复制引用
国琳,左万利,彭涛..基于隶属度的社会化网络重叠社区发现及动态集群演化分析[J].电子学报,2016,44(3):587-594,8.基金项目
国家自然科学基金（No．60973040）；国家自然科学青年基金（No．61300148）；吉林省重点科技攻关项目基金（No．20130206051GX）；吉林省科技计划青年科研基金（No．20130522112JH）；中国博士后基金项目（No．2012M510879）；吉林大学基本科研业务费科学前沿与交叉项目 （）
