Seeded graph matching via large neighborhood statistics

© 2020 Wiley Periodicals, LLC. We study a noisy graph isomorphism problem, where the goal is to perfectly recover the vertex correspondence between two edge-correlated graphs, with an initial seed set of correctly matched vertex pairs revealed as side information. We show that it is possible to achi...

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Bibliographic Details
Main Authors: Mossel, E, Xu, J
Other Authors: Massachusetts Institute of Technology. Department of Mathematics
Format: Article
Language:English
Published: Wiley 2021
Online Access:https://hdl.handle.net/1721.1/135321
Description
Summary:© 2020 Wiley Periodicals, LLC. We study a noisy graph isomorphism problem, where the goal is to perfectly recover the vertex correspondence between two edge-correlated graphs, with an initial seed set of correctly matched vertex pairs revealed as side information. We show that it is possible to achieve the information-theoretic limit of graph sparsity in time polynomial in the number of vertices n. Moreover, we show the number of seeds needed for perfect recovery in polynomial-time can be as low as (Formula presented.) in the sparse graph regime (with the average degree smaller than (Formula presented.)) and (Formula presented.) in the dense graph regime, for a small positive constant (Formula presented.). Unlike previous work on graph matching, which used small neighborhoods or small subgraphs with a logarithmic number of vertices in order to match vertices, our algorithms match vertices if their large neighborhoods have a significant overlap in the number of seeds.