Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Community Mining from Signed Social Networks
IEEE Transactions on Knowledge and Data Engineering
Parallel community detection on large networks with propinquity dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Genetic Algorithm with Local Search for Community Mining in Complex Networks
ICTAI '10 Proceedings of the 2010 22nd IEEE International Conference on Tools with Artificial Intelligence - Volume 01
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Network clustering problem (NCP) is the problem associated to the detection of network community structures. Building on Markov random walks we address this problem with a new ant colony optimization strategy, named as ACOMRW, which improves prior results on the NCP problem and does not require knowledge of the number of communities present on a given network. The framework of ant colony optimization is taken as the basic framework in the ACOMRWalgorithm. At each iteration, a Markov random walk model is taken as heuristic rule; all of the ants' local solutions are aggregated to a global one through clustering ensemble, which then will be used to update a pheromone matrix. The strategy relies on the progressive strengthening of within-community links and the weakening of between-community links. Gradually this converges to a solution where the underlying community structure of the complex network will become clearly visible. The performance of algorithm ACOMRW was tested on a set of benchmark computer-generated networks, and as well on real-world network data sets. Experimental results confirm the validity and improvements met by this approach.