Metaheuristically Optimized Multicriteria Clustering for Medium-Scale Networks
We present a highly scalable metaheuristic approach to complex network clustering. Our method uses a multicriteria construction procedure (MCP), controlled by adaptable constraints of local density and local connectivity. The input of the MCP - the permutation of vertices, is evolved using a metaheuristic based on local search. Our approach provides a favorable computational complexity of the MCP for sparse graphs and an adaptability of the constraints, since the criteria of a ”good clustering” are still not generally agreed upon in the literature. Experimental verification, regarding the quality and running time, is performed on several well-known network clustering instances, as well as on real-world social network data.
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