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Title: Matching embeddings via shuffled total least squares regression Authors:  Daniel Sussman - Boston University (United States) [presenting]
Abstract: A frequently used approach for graph matching is first to embed the networks as points in Euclidean space and then match the embeddings. We consider the case that the two graphs have related but not identical distributions that necessitate a more complex alignment in the matching step. This is related to the problem known as shuffled linear regression. We consider a modified shuffled regression setting where there is noise in both the response and the predictor variables. This setting better matches the graph matching problem and we provide convergence rates for a shuffled total least squares method in terms of the normalized Procrustes quadratic loss.