SIAM Review
On the Rank of Extreme Matrices in Semidefinite Programs and the Multiplicity of Optimal Eigenvalues
Mathematics of Operations Research
ACM Computing Surveys (CSUR)
Numerical Optimization: Theoretical and Practical Aspects (Universitext)
Numerical Optimization: Theoretical and Practical Aspects (Universitext)
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Networks: An Introduction
The dynamic competitive recommendation algorithm in social network services
Information Sciences: an International Journal
Information Sciences: an International Journal
Line orthogonality in adjacency eigenspace with application to community partition
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Maximizing modularity intensity for community partition and evolution
Information Sciences: an International Journal
Dynamic entropy based DoS attack detection method
Computers and Electrical Engineering
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The detection of network communities has attracted significant research attention lately. To discover such structures, a mathematical measure known as modularity is often used for optimization. Unfortunately, the optimization is NP-hard, and approximated solutions have to be sought for large networks. In this paper, we propose a nonlinear programming method for optimization that is based on the augmented Lagrangian technique. We further identify the inherent connection between the proposed method and positive semi-definite programming and its low-rank reduction, which helps to justify the performance of the method. Compared with previously published approaches, the proposed method is empirically efficient and effective at detecting underlying network communities.