Corpus ID: 231719365

# Random Graph Matching with Improved Noise Robustness

@article{Mao2021RandomGM,
title={Random Graph Matching with Improved Noise Robustness},
author={Cheng Mao and Mark Rudelson and Konstantin E. Tikhomirov},
journal={ArXiv},
year={2021},
volume={abs/2101.11783}
}
• Published 28 January 2021
• Computer Science, Mathematics
• ArXiv
Graph matching, also known as network alignment, refers to finding a bijection between the vertex sets of two given graphs so as to maximally align their edges. This fundamental computational problem arises frequently in multiple fields such as computer vision and biology. Recently, there has been a plethora of work studying efficient algorithms for graph matching under probabilistic models. In this work, we propose a new algorithm for graph matching: Our algorithm associates each vertex with a… Expand
3 Citations
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• Mathematics, Computer Science
• ArXiv
• 2021
It is shown how one can exactly recover the latent communities using multiple correlated graphs in parameter regimes where it is information-theoretically impossible to do so using just a single graph. Expand
Exact Matching of Random Graphs with Constant Correlation
• Mathematics, Computer Science
• ArXiv
• 2021
This is the first polynomial-time algorithm that recovers the exact matching between vertices of correlated Erdős–Rényi graphs with constant correlation with high probability, based on comparison of partition trees associated with the graph vertices. Expand
Testing network correlation efficiently via counting trees
We propose a new procedure for testing whether two networks are edge-correlated through some latent vertex correspondence. The test statistic is based on counting the co-occurrences of signed treesExpand

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