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• W3036694079 abstract "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 node embedding distributions. Specifically, we use the Wasserstein distance to measure the dissimilarity between node embeddings of different graphs. Different from prior work, we avoid pairwise calculation of distances between graphs and reduce the computational complexity from quadratic to linear in the number of graphs. WEGL calculates Monge maps from a reference distribution to each node embedding and, based on these maps, creates a fixed-sized vector representation of the graph. We evaluate our new graph embedding approach on various benchmark graph-property prediction tasks, showing state-of-the-art classification performance, while having superior computational efficiency." @default.
• W3036694079 created "2020-06-25" @default.
• W3036694079 creator A5041274548 @default.
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• W3036694079 date "2020-06-16" @default.
• W3036694079 modified "2023-09-23" @default.
• W3036694079 title "Wasserstein Embedding for Graph Learning" @default.
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