Exact Phase Transitions for Stochastic Block Models and Reconstruction on Trees

Elchanan Mossel, Allan Sly, Youngtak Sohn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

In this paper, we rigorously establish the predictions in ground breaking work in statistical physics by Decelle, Krzakala, Moore, Zdeborová (2011) regarding the block model, in particular in the case of q=3 and q=4 communities. We prove that for q=3 and q=4 there is no computational-statistical gap if the average degree is above some constant by showing that it is information theoretically impossible to detect below the Kesten-Stigum bound. We proceed by showing that for the broadcast process on Galton-Watson trees, reconstruction is impossible for q=3 and q=4 if the average degree is sufficiently large. This improves on the result of Sly (2009), who proved similar results for d-regular trees when q=3. Our analysis of the critical case q=4 provides a detailed picture showing that the tightness of the Kesten-Stigum bound in the antiferromagnetic regime depends on the average degree of the tree. We also prove that for q≥ 5, the Kesten-Stigum bound is not sharp. Our results prove conjectures of Decelle, Krzakala, Moore, Zdeborová (2011), Moore (2017), Abbe and Sandon (2018) and Ricci-Tersenghi, Semerjian, and Zdeborová (2019). Our proofs are based on a new general coupling of the tree and graph processes and on a refined analysis of the broadcast process on the tree.

Original languageEnglish (US)
Title of host publicationSTOC 2023 - Proceedings of the 55th Annual ACM Symposium on Theory of Computing
EditorsBarna Saha, Rocco A. Servedio
PublisherAssociation for Computing Machinery
Pages96-102
Number of pages7
ISBN (Electronic)9781450399135
DOIs
StatePublished - Jun 2 2023
Event55th Annual ACM Symposium on Theory of Computing, STOC 2023 - Orlando, United States
Duration: Jun 20 2023Jun 23 2023

Publication series

NameProceedings of the Annual ACM Symposium on Theory of Computing
ISSN (Print)0737-8017

Conference

Conference55th Annual ACM Symposium on Theory of Computing, STOC 2023
Country/TerritoryUnited States
CityOrlando
Period6/20/236/23/23

All Science Journal Classification (ASJC) codes

  • Software

Keywords

  • Broadcast models
  • Community detection
  • Reconstruction on trees
  • Stochastic block models

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