ansonb / FeTA_TMLR
This repository reproduces the results in the paper "How expressive are transformers in spectral domain for graphs?"(published in TMLR)
☆11Updated 2 years ago
Alternatives and similar repositories for FeTA_TMLR:
Users that are interested in FeTA_TMLR are comparing it to the libraries listed below
- PyTorch implementation of Pseudo-Riemannian Graph Convolutional Networks (NeurIPS'22))☆17Updated 7 months ago
- Code for the KDD 2021 paper 'Filtration Curves for Graph Representation'☆18Updated last year
- ☆29Updated 2 years ago
- Graph Transformers for Large Graphs☆20Updated 9 months ago
- Signal compression and reconstruction on complexes preserving topological features via Discrete Morse Theory☆11Updated 2 years ago
- Source code for "Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation" (IJCAI 2020)☆17Updated 6 months ago
- Pytorch and Tensorflow implementation of TVGNN, presented at ICML 2023.☆20Updated last year
- The implementation of HyperND from the Nonlinear Feature Diffusion on Hypergraphs paper☆13Updated 2 years ago
- PyTorch Codes for Haar Graph Pooling☆11Updated 2 years ago
- NeurIPS 2022: Tree Mover’s Distance: Bridging Graph Metrics and Stability of Graph Neural Networks☆36Updated last year
- Code for “ACE-HGNN: Adaptive Curvature ExplorationHyperbolic Graph Neural Network”☆14Updated 2 years ago
- ☆13Updated 4 years ago
- The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).☆31Updated 4 years ago
- ☆13Updated 4 years ago
- "Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data" (NeurIPS 21')☆47Updated 2 years ago
- Codebase for "Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions", ICML 2020.☆8Updated 4 years ago
- Source Code for ICML 2022 paper "Boosting Graph Structure Learning with Dummy Nodes"☆19Updated last year
- ☆13Updated last year
- Source code for the "Computationally Tractable Riemannian Manifolds for Graph Embeddings" paper☆35Updated 4 years ago
- Ultrahyperbolic Representation Learning☆12Updated 4 years ago
- The PyTorch implementation of Directed Graph Contrastive Learning (DiGCL), NeurIPS-2021☆36Updated 3 years ago
- The implementation of our AAAI 2020 paper "GSSNN: Graph Smoothing Splines Neural Network".☆20Updated 4 years ago
- Official repository for On Over-Squashing in Message Passing Neural Networks (ICML 2023)☆14Updated last year
- This repo is for source code of NeurIPS 2021 paper "Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration".☆21Updated 3 years ago
- CAT-Walk is an inducive method that learns hyperedge representations via a novel higher-order random walk, SetWalk.☆13Updated last year
- Official Code Repository for the paper "Edge Representation Learning with Hypergraphs" (NeurIPS 2021)☆52Updated last year
- This is the code of paper: Robust Mid-Pass Filtering Graph Convolutional Networks.(paper accepted by WWW2023)☆12Updated last year
- Official implementation of the ICML 2022 paper "Going Deeper into Permutation-Sensitive Graph Neural Networks"☆25Updated 2 years ago
- ☆24Updated 10 months ago
- On the Robustness of Graph Neural Diffusion to Topology Perturbations☆14Updated 2 years ago