Abstract
Given a complex biological or social network, how many clusters should it be decomposed into? We define the distance 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 for any k of the graph; it is a local attractor of i if (the set of nearest neighbors of and for any 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.
- Received 22 August 2002
DOI:https://doi.org/10.1103/PhysRevE.67.041908
©2003 American Physical Society