首页> 外文期刊>Wuhan University Journal of Natural Sciences >Identify Implicit Communities by Graph Clustering
【24h】

Identify Implicit Communities by Graph Clustering

机译:通过图聚类识别隐式社区

获取原文
获取原文并翻译 | 示例
           

摘要

How to find these communities is an important research work. Recently, community discovery are mainly categorized to HITS algorithm, bipartite cores algorithm and maximum flow/minimum cut framework. In this paper, we proposed a new method to extract communities. The MCL algorithm, which is short for the Markov Cluster Algorithm, a fast and scalable unsupervised cluster algorithm is used to extract communities. By putting mirror deleting procedure behind graph clustering, we decrease comparing cost considerably. After MCL and mirror deletion, we use community member select algorithm to produce the sets of community candidates. The experiment and results show the new method works effectively and properly.
机译:如何找到这些社区是一项重要的研究工作。最近,社区发现主要分类为HITS算法,二分核心算法和最大流量/最小切割框架。在本文中,我们提出了一种提取社区的新方法。 MCL算法是Markov聚类算法的缩写,它是一种快速且可扩展的无监督聚类算法,用于提取社区。通过将镜像删除过程放在图聚类之后,我们可以大大降低比较成本。在MCL和镜像删除之后,我们使用社区成员选择算法来生成社区候选集。实验和结果表明,该方法有效有效。

著录项

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