Network landscape from a Brownian particle's perspective.
Network landscape from a Brownian particle's perspective.
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DOI：
10.1103/physreve.67.041908
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发表时间：
2003-02
期刊：
影响因子：
--
通讯作者：
Haijun Zhou
中科院分区：
文献类型：
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作者：
Haijun Zhou
给出一个复杂的生物或社会网络，它应该被分解成多少个集群？我们定义从结点i到结点j的距离d(i，j)为一个布朗粒子从i到达j所需的平均步数。对于图的任何k，结点j是i的全局吸引子如果d(i，j)<；或=d(i，k)；如果j在E(I)(i的最近邻域集合)中且d(i，j)<；或对E(I)中的任何L，它是i的局部吸引子。基于每个节点在全局(局部)尺度上与其全局(局部)吸引子应该有很高的概率处于同一社区的直觉，我们提出了一种揭示网络社区结构的简单方法。将该方法应用于几个实际网络，并对其可能的扩展进行了讨论。
Given a complex biological or social network, how many clusters should it be decomposed into? We define the distance d(i,j) from node i to node j as the average number of steps a Brownian particle takes to reach j from i. Node j is a global attractor of i if d(i,j)< or =d(i,k) for any k of the graph; it is a local attractor of i if j in E(i) (the set of nearest neighbors of i) and d(i,j)< or =d(i,l) for any l in E(i). Based on the intuition that each node should have a high probability to be in the same community as its global (local) attractor on the global (local) scale, we present a simple method to uncover a network's community structure. This method is applied to several real networks and some discussion on its possible extensions is made.