Name
Abstract | ||
|---|---|---|
Community discovery is very important for understanding the organization or structure of a network or social system. However, it is still a very challenging problem, especially for a large-scale network. In fact, current community mining algorithms generally aim at a special kind of networks and cannot be applied to the general cases. Moreover, they are generally time-consuming. This paper proposes a Modularity Dominated Density Based Merge Search (MDDBMS) algorithm which is a further approach to the density based merge search to community mining by considering all the network as a graph of vertexes with the densities as their degrees. In fact, certain criteria are modified and the modularity is used to check whether the merge operation is needed. The experimental results on several datasets of social and protein-protein interaction (PPI) networks demonstrate that our proposed MDDBMS algorithm can obtain competitive results in comparison with current state-of-the-art community mining algorithms with much lower time consumption. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/SMC.2015.479 | 2015 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC 2015): BIG DATA ANALYTICS FOR HUMAN-CENTRIC SYSTEMS |
Keywords | DocType | ISSN |
Community discovery, social network, graph, clustering analysis, modularity | Conference | 1062-922X |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhongyu Wang | 1 | 0 | 0.34 |
| Jinwen Ma | 2 | 4 | 4.81 |