计算机应用与软件2016，Vol.33Issue(6)：37-39,43,4.DOI:10.3969/j.issn.1000-386x.2016.06.009
复杂网络中基于WCC的并行可扩展社团挖掘算法
WCC-BASED PARALLEL AND SCALABLE COMMUNITY MINING ALGORITHM IN COMPLEX NETWORKS
摘要
Abstract
Weighted community clustering (WCC ) evaluates the performance of community mining algorithms according to triangles number of a community in complex networks.In primitive WCC algorithm it has the need to compute WCC scores for all community changes in each iteration,therefore the computation burden is very heavy.In order to minimise WCC computation brought about by communities change,this paper proposes a parallel and scalable community mining algorithm.We analysed the methods of community evaluation using WCC,and proposed a parallel community mining algorithm including three stages of preprocessing,initial partitioning and partition refinement.During the process of partition refinement,since every change in community needs much computation for WCC improvements,so we proposed a WCC approximated computation algorithm based on the statistics of community.Massive experiments on real datasets show that,the proposed community mining algorithm is more accurate and has better scalability than related works.关键词
复杂网络/社团挖掘/并行算法/可扩展性Key words
Complex networks/Community mining/Parallel algorithm/Scalability分类
信息技术与安全科学引用本文复制引用
亚森·艾则孜,李卫平,郭文强..复杂网络中基于WCC的并行可扩展社团挖掘算法[J].计算机应用与软件,2016,33(6):37-39,43,4.基金项目
国家自然科学基金项目（61163066，60902074）；新疆维吾尔自治区高校科研计划科学研究重点项目（XJEDU 2013134）；国家社会科学基金项目（13CFX055）；河南省教育厅科学技术研究重点项目（14A520011）。 （）
