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[2605.31315] Graph Neural Networks Are Not Continuous Across Graph Resolutions
Abstract page for arXiv paper 2605.31315: Graph Neural Networks Are Not Continuous Across Graph Resolutions
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[2605.31013] Physics-Informed Coarsening for Multigrid Graph Neural Surrogates
Abstract page for arXiv paper 2605.31013: Physics-Informed Coarsening for Multigrid Graph Neural Surrogates
[2605.29228] Traditional machine learning vs. deep learning from dynamic graph representations of proteins' 3D folds in the task of protein structure classification
Abstract page for arXiv paper 2605.29228: Traditional machine learning vs. deep learning from dynamic graph representations of proteins' 3D folds in the task of...
[2605.27770] Sampling Triangulations and Calabi-Yau Threefolds with Autoregressive GNNs
Abstract page for arXiv paper 2605.27770: Sampling Triangulations and Calabi-Yau Threefolds with Autoregressive GNNs
[2605.28103] Benchmarking Inductive Biases for Multivariate Time-Series Anomaly Detection with a Robust Multi-View Channel-Graph Detector
Abstract page for arXiv paper 2605.28103: Benchmarking Inductive Biases for Multivariate Time-Series Anomaly Detection with a Robust Multi-View Channel-Graph De...
[2605.28578] A Generalized Tikhonov Layer for Interpretable-by-design Graph Neural Networks
Abstract page for arXiv paper 2605.28578: A Generalized Tikhonov Layer for Interpretable-by-design Graph Neural Networks
[2605.28300] T-GINEE: A Tensor-Based Multilayer Graph Representation Learning
Abstract page for arXiv paper 2605.28300: T-GINEE: A Tensor-Based Multilayer Graph Representation Learning