Bounded Projection Matrix Approximation with Applications to Community Detection
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Abstract (arXiv)
Community detection is an important problem in unsupervised learning. This paper proposes to solve a projection matrix approximation problem with an additional entrywise bounded constraint. Algorithmically, we introduce a new differentiable convex penalty and derive an alternating direction method of multipliers (ADMM) algorithm. Theoretically, we establish the convergence properties of the proposed algorithm. Numerical experiments demonstrate the superiority of our algorithm over its competitors, such as the semi-definite relaxation method and spectral clustering.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 7 of its 23 references that are in the map. First posted on arXiv 21 May 2023.
Builds on
7 of 23 references.
References · 7
- 2017Community Detection and Stochastic Block Modelscited by 317
- 2017A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applicationscited by 260
- 2017Graph Embedding Techniques, Applications, and Performance: A Surveycited by 188
- 2020Understanding graph embedding methods and their applicationscited by 50
- 2014Statistical-Computational Tradeoffs in Planted Problems and Submatrix Localization with a Growing Number of Clusters and Submatricescited by 29
- 2014On semidefinite relaxations for the block modelcited by 22
- 2022An efficient approach for nonconvex semidefinite optimization via customized alternating direction method of multiplierscited by 2
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Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.