Modularity maximization using completely positive programming

Sakineh Yazdanparast and Timothy C. Havens

Physica A: Statistical Mechanics and its Applications, 2017, vol. 471, issue C, 20-32

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.

Keywords: Community detection; Modularity maximization; Completely positive programming; Quadratic programming; Linear programming (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:471:y:2017:i:c:p:20-32

DOI: 10.1016/j.physa.2016.11.108

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