Multiscale community geometry in a network and its application
Multiscale community geometry in a network and its application
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DOI：
10.1103/physreve.86.041120
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发表时间：
2012-10-12
影响因子：
2.4
通讯作者：
Fushing, Hsieh
中科院分区：
文献类型：
--
作者：
Chen, Chen;Fushing, Hsieh
我们引入了一个基于介数的距离度量来提取局部和全局信息的每对节点（或“顶点”可互换使用）位于一个二进制网络。由于该距离然后将加权图叠加在这样的二进制网络上，因此称为数据云几何的多尺度聚类机制适用于发现二进制网络内的分层社区。该方法解决了基于模块化优化的社区发现方法的许多缺点。使用几个人为的和真实的二进制网络，我们的社区层次结构比较有利的结果来自最近提出的方法的基础上的时间尺度差异的随机游走，并已表现出显着的改进基于模块的方法，特别是在多尺度和社区的数量的确定。
We introduce a between-ness-based distance metric to extract local and global information for each pair of nodes (or "vertices" used interchangeably) located in a binary network. Since this distance then superimposes a weighted graph upon such a binary network, a multiscale clustering mechanism, called data cloud geometry, is applicable to discover hierarchical communities within a binary network. This approach resolves many shortcomings of community finding approaches, which are primarily based on modularity optimization. Using several contrived and real binary networks, our community hierarchies compare favorably with results derived from a recently proposed approach based on time-scale differences of random walks and has already demonstrated significant improvements over module-based approaches, especially on the multiscale and the determination of the number of communities.