Community Detection and Stochastic Block Models

2018 ◽  
Author(s):  
Emmanuel Abbe
Keyword(s):  
Community Detection ◽  
Block Models

scholarly journals Consistency of community detection in networks under degree-corrected stochastic block models

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

scholarly journals A scalable community detection algorithm for large graphs using stochastic block models

2018 ◽  
Vol 22 (1) ◽  
pp. 239
Author(s):  
Chengbin Peng ◽  
Zhihua Zhang ◽  
Ka-Chun Wong ◽  
Xiangliang Zhang ◽  
David E. Keyes
Keyword(s):  
Community Detection ◽  
Detection Algorithm ◽  
Large Graphs ◽  
Community Detection Algorithm ◽  
Block Models

scholarly journals A scalable community detection algorithm for large graphs using stochastic block models

2017 ◽  
Vol 21 (6) ◽  
pp. 1463-1485 ◽  
Author(s):  
Chengbin Peng ◽  
Zhihua Zhang ◽  
Ka-Chun Wong ◽  
Xiangliang Zhang ◽  
David E. Keyes
Keyword(s):  
Community Detection ◽  
Detection Algorithm ◽  
Large Graphs ◽  
Community Detection Algorithm ◽  
Block Models

scholarly journals Community detection in degree-corrected block models

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

scholarly journals Minimax rates of community detection in stochastic block models

2016 ◽  
Vol 44 (5) ◽  
pp. 2252-2280 ◽  
Author(s):  
Anderson Y. Zhang ◽  
Harrison H. Zhou
Keyword(s):  
Community Detection ◽  
Minimax Rates ◽  
Block Models

scholarly journals A spectral method for community detection in moderately sparse degree-corrected stochastic block models

2017 ◽  
Vol 49 (3) ◽  
pp. 686-721 ◽  
Author(s):  
Lennart Gulikers ◽  
Marc Lelarge ◽  
Laurent Massoulié
Keyword(s):  
Spectral Method ◽  
Community Detection ◽  
Adjacency Matrix ◽  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Block Models ◽  
Spectral Clustering Algorithm

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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