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Edge-Ratio Network Clustering by Variable Neighborhood Search

Abstract : The analysis of networks and in particular the identification of communities, or clusters, is a topic of active research with application arising in many domains. Several models were proposed for its solution. In [Cafieri et al., Phys. Rev. E 81(2):026105, 2010], a criterion is proposed for a graph bipartition to be optimal: one seeks to maximize the minimum for both classes of the bipartition of the ratio of inner edges to cut edges (edge ratio), and it is used in a hierarchical divisive algorithm for community identification in networks. In this paper, we develop a VNS-based heuristic for hierarchical divisive edge ratio network clustering. A k-neighborhood is defined as move of k entities, i.e., k entities change their membership from one to another cluster. A local search is based on 1-changes and k-changes are used for shaking the incumbent solution. Computational results on datasets from the literature validate the proposed approach.
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Sonia Cafieri, Pierre Hansen, Nenad Mladenovic. Edge-Ratio Network Clustering by Variable Neighborhood Search. European Physical Journal B: Condensed Matter and Complex Systems, Springer-Verlag, 2014, 87 (5), pp 116. ⟨10.1140/epjb/e2014-50026-4⟩. ⟨hal-00979295⟩

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