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Main Author: Majumdar, Sourav
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2507.14464
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author Majumdar, Sourav
author_facet Majumdar, Sourav
contents We propose exact conditional goodness-of-fit tests for directed mixed membership stochastic block models. Given dyad-level sender and receiver roles, the block-pair edge totals are sufficient for the block probability matrix; conditioning on these totals gives a nuisance-free uniform law on a finite fiber. This yields finite-sample randomization tests for residual sender and receiver heterogeneity, reciprocity, and directed transitive closure. The procedure uses an independent fiber sampler, Monte Carlo rank \(p\)-values, and can be applied after drawing latent block-pair assignments from the posterior distribution. Simulations and the Sampson monastery network show that the tests are calibrated under the null and diagnostically useful for directed model misspecification.
format Preprint
id arxiv_https___arxiv_org_abs_2507_14464
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Exact conditional goodness-of-fit tests for the mixed membership stochastic block model
Majumdar, Sourav
Methodology
We propose exact conditional goodness-of-fit tests for directed mixed membership stochastic block models. Given dyad-level sender and receiver roles, the block-pair edge totals are sufficient for the block probability matrix; conditioning on these totals gives a nuisance-free uniform law on a finite fiber. This yields finite-sample randomization tests for residual sender and receiver heterogeneity, reciprocity, and directed transitive closure. The procedure uses an independent fiber sampler, Monte Carlo rank \(p\)-values, and can be applied after drawing latent block-pair assignments from the posterior distribution. Simulations and the Sampson monastery network show that the tests are calibrated under the null and diagnostically useful for directed model misspecification.
title Exact conditional goodness-of-fit tests for the mixed membership stochastic block model
topic Methodology
url https://arxiv.org/abs/2507.14464