twitter-research / feature-propagationLinks
☆84Updated 3 years ago
Alternatives and similar repositories for feature-propagation
Users that are interested in feature-propagation are comparing it to the libraries listed below
Sorting:
- PyTorch Geometric Signed Directed is a signed/directed graph neural network extension library for PyTorch Geometric. The paper is accepte…☆144Updated 7 months ago
- Dynamic Graph Benchmark☆82Updated 2 years ago
- Temporal Graph Benchmark project repo☆236Updated this week
- ☆75Updated 3 years ago
- Code for our paper "Attending to Graph Transformers"☆90Updated last year
- Generating PGM Explanation for GNN predictions☆75Updated 2 years ago
- [NeurIPS 2021]: Improve the GNN expressivity and scalability by decoupling the depth and receptive field of state-of-the-art GNN architec…☆137Updated 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
- PyTorch code of "SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks"☆89Updated 3 years ago
- code for Graph Neural Networks for Link Prediction with Subgraph Sketching https://arxiv.org/abs/2209.15486☆97Updated last year
- A curated list of graph data augmentation papers.☆312Updated last year
- GraphXAI: Resource to support the development and evaluation of GNN explainers☆197Updated last year
- ☆142Updated 4 years ago
- ☆96Updated 2 years ago
- Wasserstein Weisfeiler-Lehman Graph Kernels☆84Updated last year
- Official repository for the paper "Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting" (TPAMI'22) https://arxi…☆102Updated 4 years ago
- Dir-GNN is a machine learning model that enables learning on directed graphs.☆82Updated 2 years ago
- Graph Diffusion Convolution, as proposed in "Diffusion Improves Graph Learning" (NeurIPS 2019)☆272Updated 2 years ago
- Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)☆106Updated 3 months ago
- Official code for the ICML 2021 paper "Generative Causal Explanations for Graph Neural Networks."☆68Updated 3 years ago
- Data for "Understanding Isomorphism Bias in Graph Data Sets" paper.☆89Updated 5 years ago
- Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"☆96Updated 3 years ago
- ☆155Updated 4 years ago
- Long Range Graph Benchmark, NeurIPS 2022 Track on D&B☆159Updated last year
- GraphFramEx: a systematic evaluation framework for explainability methods on GNNs☆46Updated last year
- ☆55Updated 3 years ago
- Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations), ICLR 2022☆264Updated 3 years ago
- Uncertainty Quantification over Graph with Conformalized Graph Neural Networks (NeurIPS 2023)☆82Updated 2 years ago
- An open-source implementation of SEAL for link prediction in open graph benchmark (OGB) datasets.☆238Updated 2 years ago
- ☆19Updated 2 years ago