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Poster
Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs
Pedro Mercado · Francesco Tudisco · Matthias Hein

Wed Dec 11 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #27

We study the task of semi-supervised learning on multilayer graphs by taking into account both labeled and unlabeled observations together with the information encoded by each individual graph layer. We propose a regularizer based on the generalized matrix mean, which is a one-parameter family of matrix means that includes the arithmetic, geometric and harmonic means as particular cases. We analyze it in expectation under a Multilayer Stochastic Block Model and verify numerically that it outperforms state of the art methods. Moreover, we introduce a matrix-free numerical scheme based on contour integral quadratures and Krylov subspace solvers that scales to large sparse multilayer graphs.

Author Information

Pedro Mercado (University of Tübingen)
Francesco Tudisco (University of Strathclyde)
Matthias Hein (University of Tübingen)

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