计算机应用与软件Issue(6)：130-135,6.DOI:10.3969/j.issn.1000-386x.2015.06.032
基于 MapReduce 的二分图社团发现
COMMUNITY DETECTION USING BIPARTITE GRAPH BASED ON MAPREDUCE
摘要
Abstract
Community detection is an important research means in complex networks area.However,with the growth of networks data scale,current algorithms are hard to fit rather large-scale data.In light of such case,we propose a MapReduce-based bipartite graph community detection algorithm.The proposed algorithm can be divided into two phases.The first phase is to map a bipartite graph onto a homogeneous weighted network.The second phase is to use parallel label propagation algorithm to detect the communities in the networks mapped.Experiments have been made on synthetic datasets and real-world datasets,and the proposed algorithm is compared with existing algorithms as well.Experimental result shows that,the proposed algorithm can get quite good result in some of the synthetic networks and real-world datasets,and has big improvement in algorithm efficiency than current algorithms.关键词
社团发现/二分图/MapReduceKey words
Community detection/Bipartite graph/MapReduce分类
信息技术与安全科学引用本文复制引用
王昊宇,吴斌..基于 MapReduce 的二分图社团发现[J].计算机应用与软件,2015,(6):130-135,6.基金项目
国家重点基础研究发展计划项目（2013CB329603）；国家自然科学基金项目（61074128，71231002）。 （）
