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Modularity maximization using completely positive programming

Author

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  • Yazdanparast, Sakineh
  • Havens, Timothy C.

Abstract

Community detection is one of the most prominent problems of social network analysis. In this paper, a novel method for Modularity Maximization (MM) for community detection is presented which exploits the Alternating Direction Augmented Lagrangian (ADAL) method for maximizing a generalized form of Newman’s modularity function. We first transform Newman’s modularity function into a quadratic program and then use Completely Positive Programming (CPP) to map the quadratic program to a linear program, which provides the globally optimal maximum modularity partition. In order to solve the proposed CPP problem, a closed form solution using the ADAL merged with a rank minimization approach is proposed. The performance of the proposed method is evaluated on several real-world data sets used for benchmarks community detection. Simulation results shows the proposed technique provides outstanding results in terms of modularity value for crisp partitions.

Suggested Citation

  • Yazdanparast, Sakineh & Havens, Timothy C., 2017. "Modularity maximization using completely positive programming," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 20-32.
    • Handle: RePEc:eee:phsmap:v:471:y:2017:i:c:p:20-32
    DOI: 10.1016/j.physa.2016.11.108
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    References listed on IDEAS

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