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cs.LG2024★ 1 cited
Residual Connections and Normalization Can Provably Prevent Oversmoothing in GNNs
Michael Scholkemper, Xinyi Wu, Ali Jadbabaie +1
Residual connections and normalization layers have become standard design choices for graph neural networks (GNNs), and were proposed as solutions to the mitigate the oversmoothing…
cs.CE2024★ 2 cited
A Wasserstein Graph Distance Based on Distributions of Probabilistic Node Embeddings
Michael Scholkemper, Damin Kühn, Gerion Nabbefeld +3
Distance measures between graphs are important primitives for a variety of learning tasks. In this work, we describe an unsupervised, optimal transport based approach to define a d…