Exact Phase Transitions for Stochastic Block Models and Reconstruction on Trees
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Abstract (arXiv)
In this paper we continue to rigorously establish the predictions in ground breaking work in statistical physics by Decelle, Krzakala, Moore, Zdeborov'a (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 it is information theoretically impossible to detect below the Kesten-Stigum bound. The proof is based on 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 regular trees for 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 case depends on the average degree of the tree. We also prove that for q≥ 5, the Kestin-Stigum bound is not sharp. Our results prove conjectures of Decelle, Krzakala, Moore, Zdeborov'a (2011), Moore (2017), Abbe and Sandon (2018) and Ricci-Tersenghi, Semerjian, and Zdeborov'a (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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Dec 2022.
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Citing papers per year: 2023: 1; 2024: 2; 2025: 0; 2026: 1.
4 papers.
- 2024How transformers learn structured data: insights from hierarchical filteringcited by 18
- 2023Spectral clustering in the Gaussian mixture block modelcited by 7
- 2024A computational transition for detecting correlated stochastic block models by low-degree polynomialscited by 6
- 2026Fundamental Limits of Community Detection in Contextual Multi-Layer Stochastic Block Modelscited by 2
- 2026Fundamental Limits of Community Detection in Contextual Multi-Layer Stochastic Block Models9 Feb 2026
- 2024A computational transition for detecting correlated stochastic block models by low-degree polynomials2 Sep 2024
- 2024How transformers learn structured data: insights from hierarchical filtering27 Aug 2024
- 2023Spectral clustering in the Gaussian mixture block model29 Apr 2023