The problem of aligning ErdÅ's-Rényi random graphs is a noisy, average-case version of the graph isomorphism problem, in which a pair of correlated random graphs is observed through a random permutation of their vertices. We study a polynomial time message-passing algorithm devised to solve the inference problem of partially recovering the hidden permutation, in the sparse regime with constant average degrees. We perform extensive numerical simulations to determine the range of parameters in which this algorithm achieves partial recovery. We also introduce a generalized ensemble of correlated random graphs with prescribed degree distributions, and extend the algorithm to this case.
Piccioli G., Semerjian G., Sicuro G., Zdeborova L. (2022). Aligning random graphs with a sub-tree similarity message-passing algorithm. JOURNAL OF STATISTICAL MECHANICS: THEORY AND EXPERIMENT, 2022(6), 1-44 [10.1088/1742-5468/ac70d2].
Aligning random graphs with a sub-tree similarity message-passing algorithm
Sicuro G.;
2022
Abstract
The problem of aligning ErdÅ's-Rényi random graphs is a noisy, average-case version of the graph isomorphism problem, in which a pair of correlated random graphs is observed through a random permutation of their vertices. We study a polynomial time message-passing algorithm devised to solve the inference problem of partially recovering the hidden permutation, in the sparse regime with constant average degrees. We perform extensive numerical simulations to determine the range of parameters in which this algorithm achieves partial recovery. We also introduce a generalized ensemble of correlated random graphs with prescribed degree distributions, and extend the algorithm to this case.File | Dimensione | Formato | |
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