Community Detection with a Subsampled Semidefinite Program

Autor:	Abdalla, Pedro, Bandeira, Afonso S.
Rok vydání:	2021
Předmět:	Mathematics - Optimization and Control Mathematics - Probability Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Semidefinite programming is an important tool to tackle several problems in data science and signal processing, including clustering and community detection. However, semidefinite programs are often slow in practice, so speed up techniques such as sketching are often considered. In the context of community detection in the stochastic block model, Mixon and Xie \cite{mixon2020sketching} have recently proposed a sketching framework in which a semidefinite program is solved only on a subsampled subgraph of the network, giving rise to significant computational savings. In this short paper, we provide a positive answer to a conjecture of Mixon and Xie about the statistical limits of this technique for the stochastic block model with two balanced communities. Comment: 8 pages, 0 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2102.01419 Zobrazit plný text záznamu View this record from Arxiv