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Seeded graph matching via large neighborhood statistics
Authors:Elchanan Mossel  Jiaming Xu
Abstract: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 urn:x-wiley:rsa:media:rsa20934:rsa20934-math-0001 in the sparse graph regime (with the average degree smaller than urn:x-wiley:rsa:media:rsa20934:rsa20934-math-0002) and urn:x-wiley:rsa:media:rsa20934:rsa20934-math-0003 in the dense graph regime, for a small positive constant urn:x-wiley:rsa:media:rsa20934:rsa20934-math-0004. 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.
Keywords:branching process  graph isomorphism  graph matching  subgraph count
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