Achieving Exact Cluster Recovery Threshold via Semidefinite Programming: Extensions
作者： Yihong Wu , Jiaming Xu , Bruce Hajek
DOI:
关键词:
摘要: … The result for two equal-sized clusters was originally conjectured in [2] and another resolution … We note that the only model parameter needed by the estimator (3) is the cluster size K. …
arxiv.org参考文章(41)
Brendan P. W. Ames, Robust convex relaxation for the planted clique and densest k-subgraph problems arXiv: Optimization and Control. ,(2013)
Terence Tao, None, Topics in Random Matrix Theory ,(2012)
Alexander S. Wein, Amelia Perry, A semidefinite program for unbalanced multisection in the stochastic block model arXiv: Data Structures and Algorithms. ,(2015)
Alexandre Proutière, Se-Young Yun, Accurate Community Detection in the Stochastic Block Model via Spectral Algorithms. arXiv: Social and Information Networks. ,(2014)
Afonso S. Bandeira, Random Laplacian matrices and convex relaxations arXiv: Probability. ,(2015)
Eli Upfal, Michael Mitzenmacher, Probability and Computing: Randomized Algorithms and Probabilistic Analysis ,(2005)
F. McSherry, Spectral partitioning of random graphs international conference on cluster computing. pp. 529- 537 ,(2001) , 10.1109/SFCS.2001.959929
Laurent Massoulié, Marc Lelarge, Simon Heimlicher, Community Detection in the Labelled Stochastic Block Model arXiv: Social and Information Networks. ,(2012)
Emmanuel Abbe, Colin Sandon, Community detection in general stochastic block models: fundamental limits and efficient recovery algorithms arXiv: Probability. ,(2015)
Florent Krzakala, Lenka Zdeborová, Alaa Saade, Marc Lelarge, Spectral Detection in the Censored Block Model arXiv: Social and Information Networks. ,(2015) , 10.1109/ISIT.2015.7282642