yandex-research / tabgraphsLinks
A benchmark of meaningful graph datasets with tabular node features
☆14Updated 3 months ago
Alternatives and similar repositories for tabgraphs
Users that are interested in tabgraphs are comparing it to the libraries listed below
Sorting:
- Code of "Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective" paper published in ICLR2021☆46Updated 4 years ago
- Uncertainty Quantification over Graph with Conformalized Graph Neural Networks (NeurIPS 2023)☆87Updated 2 years ago
- Official implementation of NeurIPS'21: Implicit SVD for Graph Representation Learning☆21Updated 4 years ago
- The PyTorch implementation of Directed Graph Contrastive Learning (DiGCL), NeurIPS-2021☆36Updated 4 years ago
- Variational Graph Convolutional Networks☆23Updated 5 years ago
- PyTorch implementation of Pseudo-Riemannian Graph Convolutional Networks (NeurIPS'22))☆17Updated last year
- "Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data" (NeurIPS 21')☆49Updated 3 years ago
- Locally corrected Nyström (LCN), as proposed in "Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, …☆19Updated 2 years ago
- New structural distributional shifts for evaluating graph models☆15Updated 2 years ago
- Graph Transformers for Large Graphs☆22Updated last year
- Official code for the ICML 2021 paper "Generative Causal Explanations for Graph Neural Networks."☆66Updated 3 years ago
- Code for the KDD 2021 paper 'Filtration Curves for Graph Representation'☆18Updated 2 years ago
- ☆23Updated 2 years ago
- ☆13Updated 4 years ago
- Graph transport network (GTN), as proposed in "Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, an…☆15Updated 2 years ago
- Code for reproducing experiments in "On the Ability of Graph Neural Networks to Model Interactions Between Vertices"☆25Updated 2 years ago
- Energetic GraphNeural Networks (EGNN) implementation based on Dirichlet Energy Constrained Learning.☆27Updated 4 years ago
- [ICML 2024] How Interpretable Are Interpretable Graph Neural Networks?☆15Updated last year
- ☆13Updated 7 months ago
- Official implementation of the ICML2021 paper "Elastic Graph Neural Networks"☆42Updated 4 years ago
- Simplicial neural network benchmarking software☆17Updated 3 years ago
- This is the official codebase of `Exploring Generative Neural Temporal Point Process' (Accepted by TMLR).☆21Updated 2 years ago
- Official repository for On Over-Squashing in Message Passing Neural Networks (ICML 2023)☆16Updated 2 years ago
- Repository of the paper "On the Trade-off between Over-smoothing and Over-squashing in Deep Graph Neural Networks" published in ACM CIKM …☆18Updated 2 years ago
- The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).☆35Updated 5 years ago
- ☆65Updated last month
- ☆51Updated 4 years ago
- This repository reproduces the results in the paper "How expressive are transformers in spectral domain for graphs?"(published in TMLR)☆12Updated 3 years ago
- This is the official code repository for "Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs".☆90Updated last year
- The open source code for ICDM2022 paper "Unifying Graph Contrastive Learning with Flexible Contextual Scopes"☆21Updated 3 years ago