Showing 1–20 of 65 results
/ Date/ Name
Dec 18, 2021Weisfeiler and Leman go Machine Learning: The Story so farNov 30, 2022Weisfeiler and Leman Go RelationalOct 14, 2019Temporal Graph Kernels for Classifying Dissemination ProcessesApr 2, 2019Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddingsJul 1, 2025Understanding Generalization in Node and Link PredictionFeb 4, 2025Towards graph neural networks for provably solving convex optimization problemsJun 2, 2025Principled Data Augmentation for Learning to Solve Quadratic Programming ProblemsNov 11, 2025Generalizable Insights for Graph Transformers in Theory and PracticeMay 27, 2024Probabilistic Graph Rewiring via Virtual NodesApr 30, 2026On the Expressive Power of GNNs to Solve Linear SDPsMar 7, 2017Global Weisfeiler-Lehman Graph KernelsFeb 12, 2024Weisfeiler-Leman at the margin: When more expressivity mattersFeb 3, 2024Future Directions in the Theory of Graph Machine LearningJan 20, 2026Principled Latent Diffusion for Graphs via Laplacian AutoencodersJul 16, 2020TUDataset: A collection of benchmark datasets for learning with graphsOct 1, 2021Reconstruction for Powerful Graph RepresentationsJun 3, 2021Cosmic ray radiography of a human phantomMay 27, 2022MIP-GNN: A Data-Driven Framework for Guiding Combinatorial SolversMar 25, 2022SpeqNets: Sparsity-aware Permutation-equivariant Graph NetworksMar 28, 2019A Survey on Graph Kernels