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Found 3 papers, 1 papers with code
SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases
Furthermore, we offer theoretical insights into SEGNO, highlighting that it can learn a unique trajectory between adjacent states, which is crucial for model generalization.
Handling Missing Data via Max-Entropy Regularized Graph Autoencoder
In this work, we present a regularized graph autoencoder for graph attribute imputation, named MEGAE, which aims at mitigating spectral concentration problem by maximizing the graph spectral entropy.
Wiener Graph Deconvolutional Network Improves Graph Self-Supervised Learning
Graph self-supervised learning (SSL) has been vastly employed to learn representations from unlabeled graphs.
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