Community Detection in General Stochastic Block models: Fundamental Limits and Efficient Algorithms for Recovery

By Emmanuel Abbe, Colin Sandon. IEEE Annual Symposium on Foundations of Computer Science, 2015

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Gradient Coding Using the Stochastic Block Model (2018, background): “Then weak reconstruction is possible exactly when b ) 2 > b ) [14].”
Avoiding Imposters and Delinquents: Adversarial Crowdsourcing and Peer Prediction (2016, background): “…corresponds to the sparse case where all nodes have constant degree, which has recently seen great interest (Decelle et al., 2011; Mossel et al., 2012; 2013b;a; Massoulié, 2014; Guédon and Vershynin, 2014; Mossel et al., 2015; Chin et al., 2015; Abbe and Sandon, 2015; Makarychev et al., 2015).”
Graph Clustering with Graph Neural Networks (2020, methodology): “Thus, the SBM has been used to analyze graph clustering algorithms both theoretically (Bickel and Chen, 2009; Abbe and Sandon, 2015) and empirically (Mothe et al.”
Statistical Inference on Random Dot Product Graphs: a Survey (2017, background): “In the study of stochastic blockmodels, there has been a recent push to understand the fundamental information-theoretic limits for community detection and graph partitioning [1, 2, 71, 72].”