摘要
社团发现是复杂网络领域的一个重要的研究手段。随着网络数据规模的不断增大,现有算法难以适应较大的数据规模。针对这种情况,提出一种基于MapReduce的二分图社团发现算法。提出的算法可以分为两个阶段,第一个阶段将一个二分图映射为一个同质加权网络。第二个阶段利用并行化的标签传播算法来检测映射后的网络中的社团结构。在人工数据集和现实数据集中进行实验,并将提出的算法与现有的算法进行对比。实验结果表明,所提出的算法能在部分人工网络以及现实数据集中取得很好的效果,并且在算法效率上,比现有算法有很大的提高。
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.
出处
《计算机应用与软件》
CSCD
2015年第6期130-135,共6页
Computer Applications and Software
基金
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)
