Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5102763 | Physica A: Statistical Mechanics and its Applications | 2017 | 15 Pages |
Abstract
We present a novel clustering algorithm for community detection, based on the dynamics towards consensus and spatial transformation. The community detection problem is translated to a clustering problem in the N-dimensional Euclidean space by three stages: (1) the dynamics running on a network is emulated to a procedure of gas diffusion in a finite space; (2) the pressure distribution vectors are used to describe the influence that each node exerts on the whole network; (3) the similarity measures between two nodes are quantified in the N-dimensional Euclidean space by k-Nearest Neighbors method. After such steps, we could merge clusters according to their similarity distances and show the community structure of a network by a hierarchical clustering tree. Tests on several benchmark networks are presented and the results show the effectiveness and reliability of our algorithm.
Related Topics
Physical Sciences and Engineering
Mathematics
Mathematical Physics
Authors
Bo Yang, He He, Xiaoming Hu,