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We present Wasserstein Embedding for Graph Learning (WEGL), a novel and fast framework for embedding entire graphs in a vector space, in which various machine learning models are applicable for graph-level prediction tasks.
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A linear optimal transportation framework for quantifying and visualizing variations in sets of images
Wei Wang, Dejan Slepčev, Saurav Basu, John A Ozolek, and Gustavo K Rohde · 2013
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Pinar Yanardag and SVN Vishwanathan · 2015
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Soheil Kolouri, Akif B Tosun, John A Ozolek, and Gustavo K Rohde · 2016
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Risi Kondor and Horace Pan · 2016
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Nils M Kriege, Pierre-Louis Giscard, and Richard Wilson · 2016
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Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Matteo Togninalli, Elisabetta Ghisu, Felipe Llinares-López, Bastian Rieck, and Karsten Borgwardt · 2019
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How powerful are graph neural networks?
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Optimal transport graph neural networks
Gary Bécigneul, Octavian-Eugen Ganea, Benson Chen, Regina Barzilay, and Tommi Jaakkola · 2020
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Graphnorm: A principled approach to accelerating graph neural network training
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Large-scale analysis of disease pathways in the human interactome
Monica Agrawal, Marinka Zitnik, and Jure Leskovec · 2018
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Nicolas Courty, Rémi Flamary, and Mélanie Ducoffe · 2018
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Strategies for pre-training graph neural networks
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