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Conference Papers Year : 2020

From tree matching to sparse graph alignment


In this paper we consider alignment of sparse graphs, for which we introduce the Neighborhood Tree Matching Algorithm (NTMA). For correlated Erd\H{o}s-R\'{e}nyi random graphs, we prove that the algorithm returns -- in polynomial time -- a positive fraction of correctly matched vertices, and a vanishing fraction of mismatches. This result holds with average degree of the graphs in $O(1)$ and correlation parameter $s$ that can be bounded away from 1, conditions under which random graph alignment is particularly challenging. As a byproduct of the analysis we introduce a matching metric between trees and characterize it for several models of correlated random trees. These results may be of independent interest, yielding for instance efficient tests for determining whether two random trees are correlated or independent.
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hal-02941092 , version 1 (16-09-2020)



Luca Ganassali, Laurent Massoulié. From tree matching to sparse graph alignment. Conference on Learning Theory (COLT) 2020, Jul 2020, Graz, Austria. ⟨hal-02941092⟩
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