Phase Transition for Stochastic Block Model with more than $\sqrt{n}$ Communities
By: Alexandra Carpentier, Christophe Giraud, Nicolas Verzelen
Potential Business Impact:
Finds hidden groups in complex networks.
Predictions from statistical physics postulate that recovery of the communities in Stochastic Block Model (SBM) is possible in polynomial time above, and only above, the Kesten-Stigum (KS) threshold. This conjecture has given rise to a rich literature, proving that non-trivial community recovery is indeed possible in SBM above the KS threshold, as long as the number $K$ of communities remains smaller than $\sqrt{n}$, where $n$ is the number of nodes in the observed graph. Failure of low-degree polynomials below the KS threshold was also proven when $K=o(\sqrt{n})$. When $K\geq \sqrt{n}$, Chin et al.(2025) recently prove that, in a sparse regime, community recovery in polynomial time is possible below the KS threshold by counting non-backtracking paths. This breakthrough result lead them to postulate a new threshold for the many communities regime $K\geq \sqrt{n}$. In this work, we provide evidences that confirm their conjecture for $K\geq \sqrt{n}$: 1- We prove that, for any density of the graph, low-degree polynomials fail to recover communities below the threshold postulated by Chin et al.(2025); 2- We prove that community recovery is possible in polynomial time above the postulated threshold, not only in the sparse regime of~Chin et al., but also in some (but not all) moderately sparse regimes by essentially counting clique occurence in the observed graph.
Similar Papers
Phase Transition for Stochastic Block Model with more than $\sqrt{n}$ Communities (II)
Machine Learning (Stat)
Finds hidden groups in large networks faster.
Stochastic block models with many communities and the Kesten--Stigum bound
Probability
Finds hidden groups in connected data.
Sharp exact recovery threshold for two-community Euclidean random graphs
Social and Information Networks
Finds hidden groups in connected things.