2003/02/11 by Haijun Zhou · 1 citation
Physics and Astronomy · #physics.bio-ph
paper · pdf · doi:10.1103/physreve.67.041908
published as Physical Review E 67: 041908 (2003) · 5 pages, 4 color-figures. REVTeX 4 format. To appear in PRE
arxiv created 2003/02/11 · arxiv updated 2009/12/01
Given a complex biological or social network, how many clusters should it be decomposed into? We define the distance di,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 di,j≤ di,k for any k of the graph; it is a local attractor of i, if j∈ Ei (the set of nearest-neighbors of i) and di,j≤ di,l for any l∈ Ei. 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.