A goodness-of-fit test for stochastic block models

By Jing Lei. 2014

207 citations recorded by the source corpus.

The stochastic block model is a popular tool for studying community structures in network data. We develop a goodness-of-fit test for the stochastic block model. The test statistic is based on the largest singular value of a residual matrix obtained by subtracting the estimated block mean effect from the adjacency matrix. Asymptotic null distribution is obtained using recent advances in random matrix theory. The test is proved to have full power against alternative models with finer structures. These results naturally lead to a consistent sequential testing estimate of the number of communities.

Subfields: Statistical and Nonlinear Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/439aeddd3aeeda8fc0d03a7c11c4ad2c49d8b3fb

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In Statistical and Nonlinear Physics

In Statistics and Probability

Citations in the corpus

Statistical Inference on Random Dot Product Graphs: a Survey (2017, background): “We stress that aside from specific cases (see [39], [102], [56]), limiting distributions for eigenvalues and eigenvectors of random graphs are notably elusive.”