scholarly journals Rate optimal Chernoff bound and application to community detection in the stochastic block models

2020 ◽  
Vol 14 (1) ◽  
pp. 1302-1347
Author(s):  
Zhixin Zhou ◽  
Ping Li

2012 ◽  
Vol 40 (4) ◽  
pp. 2266-2292 ◽  
Author(s):  
Yunpeng Zhao ◽  
Elizaveta Levina ◽  
Ji Zhu


2018 ◽  
Vol 22 (1) ◽  
pp. 239
Author(s):  
Chengbin Peng ◽  
Zhihua Zhang ◽  
Ka-Chun Wong ◽  
Xiangliang Zhang ◽  
David E. Keyes


2017 ◽  
Vol 21 (6) ◽  
pp. 1463-1485 ◽  
Author(s):  
Chengbin Peng ◽  
Zhihua Zhang ◽  
Ka-Chun Wong ◽  
Xiangliang Zhang ◽  
David E. Keyes


Author(s):  
Akshay Gadde ◽  
Eyal En Gad ◽  
Salman Avestimehr ◽  
Antonio Ortega


2020 ◽  
Vol 102 (3) ◽  
Author(s):  
Tzu-Chi Yen ◽  
Daniel B. Larremore


2018 ◽  
Vol 46 (5) ◽  
pp. 2153-2185 ◽  
Author(s):  
Chao Gao ◽  
Zongming Ma ◽  
Anderson Y. Zhang ◽  
Harrison H. Zhou




2016 ◽  
Vol 44 (5) ◽  
pp. 2252-2280 ◽  
Author(s):  
Anderson Y. Zhang ◽  
Harrison H. Zhou


2017 ◽  
Vol 49 (3) ◽  
pp. 686-721 ◽  
Author(s):  
Lennart Gulikers ◽  
Marc Lelarge ◽  
Laurent Massoulié

AbstractWe consider community detection in degree-corrected stochastic block models. We propose a spectral clustering algorithm based on a suitably normalized adjacency matrix. We show that this algorithm consistently recovers the block membership of all but a vanishing fraction of nodes, in the regime where the lowest degree is of order log(n) or higher. Recovery succeeds even for very heterogeneous degree distributions. The algorithm does not rely on parameters as input. In particular, it does not need to know the number of communities.



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