nec-research / Adaptive-Message-PassingLinks
Official Repository of Adaptive Message Passing
☆19Updated 4 months ago
Alternatives and similar repositories for Adaptive-Message-Passing
Users that are interested in Adaptive-Message-Passing are comparing it to the libraries listed below
Sorting:
- Uncertainty Quantification over Graph with Conformalized Graph Neural Networks (NeurIPS 2023)☆83Updated 2 years ago
- ☆31Updated last year
- ☆42Updated 3 years ago
- This repository holds code and other relevant files for the Learning on Graphs 2022 tutorial "Graph Rewiring: From Theory to Applications…☆54Updated 2 years ago
- Code for our paper "Attending to Graph Transformers"☆91Updated last year
- ☆23Updated last year
- Official repository for On Over-Squashing in Message Passing Neural Networks (ICML 2023)☆16Updated 2 years ago
- Graph Positional and Structural Encoder☆53Updated 9 months ago
- Equivariant Subgraph Aggregation Networks (ICLR 2022 Spotlight)☆89Updated 2 years ago
- ☆61Updated last year
- Code for the KDD 2021 paper 'Filtration Curves for Graph Representation'☆18Updated 2 years ago
- GraphCON (ICML 2022)☆59Updated 3 years ago
- Long Range Graph Benchmark, NeurIPS 2022 Track on D&B☆160Updated last year
- Gradient gating (ICLR 2023)☆55Updated 2 years ago
- All graph/GNN papers accepted at NeurIPS 2024.☆84Updated last year
- A library for subgraph GNN based on pyg☆40Updated 11 months ago
- [NeurIPS'21] Higher-order Transformers for sets, graphs, and hypergraphs, in PyTorch☆68Updated 2 years ago
- Simplicial neural networks (SNNs), a generalization of graph neural networks to data that live on a class of topological spaces called si…☆81Updated 4 years ago
- Code for reproducing experiments in "On the Ability of Graph Neural Networks to Model Interactions Between Vertices"☆25Updated 2 years ago
- Official repository for the Topological Deep Learning Challenge 2024, organized by TAG-DS & PyT-Team and hosted by GRaM Workshop @ ICML 2…☆40Updated 8 months ago
- A Note On Over-Smoothing for Graph Neural Network☆20Updated 5 years ago
- Code of "Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective" paper published in ICLR2021☆46Updated 4 years ago
- A collection of papers studying/improving the expressiveness of graph neural networks (GNNs)☆124Updated 2 years ago
- Simplicial neural network benchmarking software☆17Updated 3 years ago
- Code and dataset to test empirically the expressive power of graph pooling operators presented as presented at NeurIPS 2023☆36Updated 2 years ago
- GraphFramEx: a systematic evaluation framework for explainability methods on GNNs☆46Updated last year
- Message Passing Neural Networks for Simplicial and Cell Complexes☆164Updated 2 years ago
- Exphormer: Sparse Transformer for Graphs☆188Updated last year
- ☆80Updated 2 years ago
- Dir-GNN is a machine learning model that enables learning on directed graphs.☆83Updated 2 years ago