Partial Recovery in the Graph Alignment Problem

July 01, 2020 ยท Declared Dead ยท ๐Ÿ› Operational Research

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Authors Georgina Hall, Laurent Massouliรฉ arXiv ID 2007.00533 Category stat.ML: Machine Learning (Stat) Cross-listed cs.DS, cs.LG, cs.SI, math.PR Citations 49 Venue Operational Research Last Checked 5 months ago
Abstract
In this paper, we consider the graph alignment problem, which is the problem of recovering, given two graphs, a one-to-one mapping between nodes that maximizes edge overlap. This problem can be viewed as a noisy version of the well-known graph isomorphism problem and appears in many applications, including social network deanonymization and cellular biology. Our focus here is on partial recovery, i.e., we look for a one-to-one mapping which is correct on a fraction of the nodes of the graph rather than on all of them, and we assume that the two input graphs to the problem are correlated Erdล‘s-Rรฉnyi graphs of parameters $(n,q,s)$. Our main contribution is then to give necessary and sufficient conditions on $(n,q,s)$ under which partial recovery is possible with high probability as the number of nodes $n$ goes to infinity. In particular, we show that it is possible to achieve partial recovery in the $nqs=ฮ˜(1)$ regime under certain additional assumptions.
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