Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5102954 | Physica A: Statistical Mechanics and its Applications | 2017 | 20 Pages |
Abstract
Community detection brings plenty of considerable problems, which has attracted more attention for many years. This paper develops a new framework, which tries to measure the interior and the exterior of a community based on a same metric, complete graph model. In particular, the exterior is modeled as a complete bipartite. We partition a network into subnetworks by maximizing the difference between the interior and the exterior of the subnetworks. In addition, we compare our approach with some state of the art methods on computer-generated networks based on the LFR benchmark as well as real-world networks. The experimental results indicate that our approach obtains better results for community detection, is capable of splitting irregular networks and achieves perfect results on the karate network and the dolphin network.
Related Topics
Physical Sciences and Engineering
Mathematics
Mathematical Physics
Authors
Peng Gang Sun, Xiya Sun,