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We presented our work on “Wasserstein Embedding for Graph Learning” at ICLR 2021.

Jun. 21, 2021—Wasserstein Embedding for Graph Learning 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. We leverage new insights on defining similarity between graphs as a function of the similarity between their...