Abstract
Various methods for analyzing networks have been proposed. Among them, methods for community detection based on network structures are important for making networks simple and easy to understand. As an attempt to incorporate background knowledge of given networks, a method known as constrained community detection has been proposed recently. Constrained community detection shows robust performance on noisy data since it uses background knowledge. In particular, methods for community detection based on constrained Hamiltonian have advantages of flexibility in output results. In this paper, we propose a method for accelerating the speed of constrained community detection based on Hamiltonian. Our optimization method is a variant of Blondel’s Louvain method which is well-known for its computational efficiency. Our experiments showed that our proposed method is superior in terms of computational time, and its accuracy is almost equal to the existing method based on simulated annealing under the same conditions. Our proposed method enables us to perform constrained community detection in larger networks compared with existing methods. Moreover, we compared the strategies of adding constraints incrementally in the process of constrained community detection.
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Nakata, K., Murata, T. (2015). Fast Optimization of Hamiltonian for Constrained Community Detection. In: Mangioni, G., Simini, F., Uzzo, S., Wang, D. (eds) Complex Networks VI. Studies in Computational Intelligence, vol 597. Springer, Cham. https://doi.org/10.1007/978-3-319-16112-9_8
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DOI: https://doi.org/10.1007/978-3-319-16112-9_8
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16111-2
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