Partial Recovery in the Graph Alignment Problem
July 01, 2020 ยท Declared Dead ยท ๐ 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 Erdลs-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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