mlsanityData to 14 Oct 2026

Partial Recovery in the Graph Alignment Problem

arXiv 2007.00533 ↗First posted 1 Jul 2020stat.ML cs.DS cs.LGcited by 27

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Abstract (arXiv)

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 ErdHos-R'enyi 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=Theta(1) regime under certain additional assumptions.

Key facts. Cited by 27 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Jul 2020.

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Citing papers per year: 2021: 6; 2022: 8; 2023: 6; 2024: 5; 2025: 1; 2026: 1.

27 papers.

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  1. 2021Settling the Sharp Reconstruction Thresholds of Random Graph Matchingcited by 45
  2. 2022Random graph matching at Otter's threshold via counting chandelierscited by 25
  3. 2021Correlated Stochastic Block Models: Exact Graph Matching with Applications to Recovering Communitiescited by 22
  4. 2021Testing network correlation efficiently via counting treescited by 22
  5. 2022Random Graph Matching in Geometric Models: the Case of Complete Graphscited by 22
  6. 2021Impossibility of Partial Recovery in the Graph Alignment Problemcited by 21
  7. 2021Random Graph Matching with Improved Noise Robustnesscited by 16
  8. 2022Matching recovery threshold for correlated random graphscited by 16
  9. 2021Correlation detection in trees for planted graph alignmentcited by 14
  10. 2022Detection threshold for correlated ErdHos-R'enyi graphs via densest subgraphscited by 13
  11. 2022Statistical limits of correlation detection in treescited by 12
  12. 2023A polynomial-time iterative algorithm for random graph matching with non-vanishing correlationcited by 9
15 more
  1. 2022Exact Community Recovery in Correlated Stochastic Block Modelscited by 8
  2. 2022A polynomial time iterative algorithm for matching Gaussian matrices with non-vanishing correlationcited by 8
  3. 2023Matching Correlated Inhomogeneous Random Graphs using the k-core Estimatorcited by 6
  4. 2024The Umeyama algorithm for matching correlated Gaussian geometric models in the low-dimensional regimecited by 6
  5. 2024A computational transition for detecting correlated stochastic block models by low-degree polynomialscited by 6
  6. 2023Efficiently matching random inhomogeneous graphs via degree profilescited by 5
  7. 2022Seeded graph matching for the correlated Gaussian Wigner model via the projected power methodcited by 4
  8. 2023Gaussian Database Alignment and Gaussian Planted Matchingcited by 3
  9. 2023Graph Matching via convex relaxation to the simplexcited by 3
  10. 2025Computational Lower Bounds for Correlated Random Graphs via Algorithmic Contiguitycited by 3
  11. 2023Covariance alignment: from maximum likelihood estimation to Gromov-Wassersteincited by 1
  12. 2024Information-Theoretic Thresholds for the Alignments of Partially Correlated Graphscited by 0
  13. 2024Testing Dependency of Weighted Random Graphscited by 0
  14. 2024Robust Random Graph Matching in Dense Graphs via an Approximate Message Passing Type Algorithmcited by 0
  15. 2026Sharp Statistical Thresholds for Attributed Gaussian Graph Matchingcited by 0