Exploring triad-rich substructures by graph-theoretic characterizations in complex networks
Abstract
One of the most important problems in complex networks is how to detect communities accurately. The main challenge lies in the fact that traditional definition about communities does not always capture the intrinsic features of communities. Motivated by the observation that communities in PPI networks tend to consist of an abundance of interacting triad motifs, we define a 2-club substructure with diameter 2 possessing triad-rich property to describe a community. Based on the triad-rich substructure, we design a DIVision Algorithm using our proposed edge Niche Centrality DIVANC to detect communities effectively in complex networks. We also extend DIVANC to detect overlapping communities by proposing a simple 2-hop overlapping strategy. To verify the effectiveness of triad-rich substructures, we compare DIVANC with existing algorithms on PPI networks, LFR synthetic networks and football networks. The experimental results show that DIVANC outperforms most other algorithms significantly and, in particular, can detect sparse communities.
- Publication:
-
Physica A Statistical Mechanics and its Applications
- Pub Date:
- February 2017
- DOI:
- 10.1016/j.physa.2016.10.021
- arXiv:
- arXiv:1602.05286
- Bibcode:
- 2017PhyA..468...53J
- Keywords:
-
- Complex networks;
- Community detection;
- Triad-rich substructure;
- Graph-theoretic characterizations;
- Edge niche centrality;
- Overlapping;
- Physics - Physics and Society;
- Computer Science - Social and Information Networks
- E-Print:
- 41 pages, 14 figures, and now underreviewing by Journal of Statistical Mechanics: Theory and Experiment