Modularity maximization using completely positive programming
Document Type
Article
Publication Date
11-23-2016
Department
Department of Electrical and Computer Engineering; Center for Data Sciences
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.
Publication Title
Physica A: Statistical Mechanics and its Applications
Recommended Citation
Yazdanparast, S.,
&
Havens, T. C.
(2016).
Modularity maximization using completely positive programming.
Physica A: Statistical Mechanics and its Applications,
471, 20-32.
http://doi.org/10.1016/j.physa.2016.11.108
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/1010