A cutting plane algorithm for a clustering problem
Mathematical Programming: Series A and B
Facets of the clique partitioning polytope
Mathematical Programming: Series A and B
The Stanford GraphBase: a platform for combinatorial computing
The Stanford GraphBase: a platform for combinatorial computing
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
IEEE Transactions on Knowledge and Data Engineering
A Scalable Multilevel Algorithm for Graph Clustering and Community Structure Detection
Algorithms and Models for the Web-Graph
Multi-level Algorithms for Modularity Clustering
SEA '09 Proceedings of the 8th International Symposium on Experimental Algorithms
Networks: An Introduction
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Heuristics are widely applied to modularity maximization models for the identification of communities in complex networks. We present an approach to be applied as a post-processing to heuristic methods in order to improve their performances. Starting from a given partition, we test with an exact algorithm for bipartitioning if it is worthwhile to split some communities or to merge two of them. A combination of merge and split actions is also performed. Computational experiments show that the proposed approach is effective in improving heuristic results.