Modularity maximization using completely positive programming
Department of Electrical and Computer Engineering, Center for Data Sciences
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.
Physica A: Statistical Mechanics and its Applications
Havens, T. C.
Modularity maximization using completely positive programming.
Physica A: Statistical Mechanics and its Applications,
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