Fast Optimization of Hamiltonian for Constrained Community Detection

  • Keisuke Nakata
  • Tsuyoshi Murata
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 597)

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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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Keisuke Nakata
    • 1
  • Tsuyoshi Murata
    • 1
  1. 1.Department of Computer Science, Graduate School of Information Science and EngineeringTokyo Institute of TechnologyMeguroJapan

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