Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
418314 | Discrete Applied Mathematics | 2014 | 11 Pages |
Abstract
The community detection problem in networks consists of determining a clustering of “related” vertices in a graph or network. Nowadays, studies involving this problem are primarily composed of modularity maximization based heuristics. In this paper, the author proposes a spectral heuristic based on a measure known as clustering coefficient to detect communities in networks. This measure favors clusterings with a strong neighborhood structure inside clusters, apparently, overcoming the scale deficiency of the modularity maximization problem. The computational experiments indicate a very successful performance by the proposed heuristic in comparison with other community detection heuristics in the literature.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Mariá C.V. Nascimento,