• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Journal of Innovative Computing Information and Control >MR-ECOCD: AN EDGE CLUSTERING ALGORITHM FOR OVERLAPPING COMMUNITY DETECTION ON LARGE-SCALE NETWORK USING MAPREDUCE
【24h】

MR-ECOCD: AN EDGE CLUSTERING ALGORITHM FOR OVERLAPPING COMMUNITY DETECTION ON LARGE-SCALE NETWORK USING MAPREDUCE

机译：MR-ECOCD：一种基于映射的大型网络重叠社区检测的边缘聚类算法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Overlapping community detection is progressively becoming an important issue in complex networks. Many in-memory overlapping community detection algorithms have been proposed for graphs with thousands of nodes. However, analyzing massive graphs with millions of nodes is impossible for the traditional algorithm. In this paper, we propose MR-ECOCD, a novel distributed computation algorithm using MapReduce to detect overlapping communities efficiently on large-scale network. Firstly, the similarities of all adjacent edges are calculated by SimilarityMap algorithm to measure the distance of edges. Secondly, we define the direct edge communities (DEC) and mergeable direct edge communities (MDEC) based on edge density clustering method. Then, MarkMap algorithm and ClusteringReduce algorithm are designed to mark DEC and merge MDEC respectively for getting finally edge communities (FEC). Finally, we transform the FEC into node communities, and a node is an overlapping node in node communities if it belongs to different edges in different FEC. MR-ECOCD consists of four major stages, and all operations are executed in parallel using MapReduce. Extensive experiments show that our algorithm can effectively and fast detect overlapping communities.
机译：重叠的社区检测正逐渐成为复杂网络中的重要问题。对于具有数千个节点的图，已经提出了许多内存中重叠社区检测算法。但是，对于传统算法而言，分析具有数百万个节点的海图是不可能的。在本文中，我们提出了一种MR-ECOCD，它是一种使用MapReduce的新型分布式计算算法，可以有效地检测大规模网络上的重叠社区。首先，利用SimilarityMap算法计算所有相邻边缘的相似度，以测量边缘的距离。其次，我们基于边缘密度聚类方法定义了直接边缘社区（DEC）和可合并的直接边缘社区（MDEC）。然后，设计了MarkMap算法和ClusteringReduce算法分别标记DEC和合并MDEC，以最终得到边缘社区（FEC）。最后，我们将FEC转换为节点社区，如果节点属于不同FEC中的不同边缘，则该节点是节点社区中的重叠节点。 MR-ECOCD包含四个主要阶段，所有操作都使用MapReduce并行执行。大量实验表明，我们的算法可以有效，快速地检测出重叠社区。

著录项

  • 来源
  • 作者

    Haitao He; Peng Zhang; Jun Dong; Jiadong Ren;

  • 作者单位

    College of Information Science and Engineering Yanshan University,The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province No. 438, Hebei Ave., Qinhuangdao 066004, P. R. China;

    College of Information Science and Engineering Yanshan University,The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province No. 438, Hebei Ave., Qinhuangdao 066004, P. R. China;

    College of Information Science and Engineering Yanshan University,The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province No. 438, Hebei Ave., Qinhuangdao 066004, P. R. China;

    College of Information Science and Engineering Yanshan University,The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province No. 438, Hebei Ave., Qinhuangdao 066004, P. R. China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Overlapping community detection; MapReduce; Large-scale networks; Edge clustering;

    机译：社区检测重叠;MapReduce;大型网络;边缘聚类;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号