
Seeded Graph Matching via Large Neighborhood Statistics
We study a well known noisy model of the graph isomorphism problem. In t...
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ManytoMany Graph Matching: a Continuous Relaxation Approach
Graphs provide an efficient tool for object representation in various co...
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Tractable Graph Matching via Soft Seeding
The graph matching problem aims to discover a latent correspondence betw...
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Sparse Partial Least Squares for Coarse Noisy Graph Alignment
Graph signal processing (GSP) provides a powerful framework for analyzin...
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Maximum Likelihood Estimation and Graph Matching in Errorfully Observed Networks
Given a pair of graphs with the same number of vertices, the inexact gra...
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Information Recovery in Shuffled Graphs via Graph Matching
While many multiple graph inference methodologies operate under the impl...
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A Note on Toroidal MaxwellCremona Correspondences
We explore toroidal analogues of the MaxwellCremona correspondence. Eri...
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Matched Filters for Noisy Induced Subgraph Detection
We consider the problem of finding the vertex correspondence between two graphs with different number of vertices where the smaller graph is still potentially large. We propose a solution to this problem via a graph matching matched filter: padding the smaller graph in different ways and then using graph matching methods to align it to the larger network. Under a statistical model for correlated pairs of graphs, which yields a noisy copy of the small graph within the larger graph, the resulting optimization problem can be guaranteed to recover the true vertex correspondence between the networks, though there are currently no efficient algorithms for solving this problem. We consider an approach that exploits a partially known correspondence and show via varied simulations and applications to the Drosophila connectome that in practice this approach can achieve good performance.
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