maxiaoba / GRAPE
☆133Updated 3 years ago
Related projects: ⓘ
- Code & data accompanying the NeurIPS 2020 paper "Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddin…☆219Updated last year
- Generating PGM Explanation for GNN predictions☆72Updated last year
- AAAI'21: Data Augmentation for Graph Neural Networks☆185Updated 4 months ago
- Source code from the NeurIPS 2019 workshop article "Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks" (G. Salha, R…☆132Updated 3 years ago
- PyTorch implementation of "Graph Convolutional Networks for Graphs Containing Missing Features"☆46Updated 7 months ago
- Graph Representation Learning via Graphical Mutual Information Maximization☆109Updated 4 years ago
- Parameterized Explainer for Graph Neural Network☆123Updated 6 months ago
- Graph Diffusion Convolution, as proposed in "Diffusion Improves Graph Learning" (NeurIPS 2019)☆264Updated last year
- ☆72Updated 3 years ago
- Source code and dataset of the NeurIPS 2020 paper "Graph Random Neural Network for Semi-Supervised Learning on Graphs"☆202Updated last year
- Bayesian Graph Neural Networks with Adaptive Connection Sampling - Pytorch☆53Updated 4 years ago
- Learning Discrete Structures for Graph Neural Networks (TensorFlow implementation)☆190Updated 6 months ago
- [KDD 2023] What’s Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders☆73Updated 9 months ago
- Official repository for the paper "Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting" (TPAMI'22) https://arxi…☆94Updated 3 years ago
- [ICLR 2021] How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision☆150Updated last year
- ☆253Updated 2 years ago
- Graph Information Bottleneck (GIB) for learning minimal sufficient structural and feature information using GNNs☆123Updated last year
- ☆128Updated last year
- [WWW'22] Towards Unsupervised Deep Graph Structure Learning☆129Updated last year
- PyTorch implementation of BGRL (https://arxiv.org/abs/2102.06514)☆79Updated last year
- This is a Pytorch implementation of GraphLIME☆83Updated 2 years ago
- [ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.☆156Updated 7 months ago
- ☆89Updated last year
- PyTorch code of "SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks"☆85Updated 2 years ago
- Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)☆96Updated 2 years ago
- ☆142Updated 3 years ago
- Official implementation of our FLAG paper (CVPR2022)☆139Updated 2 years ago
- Papers about developing deep Graph Neural Networks (GNNs)☆300Updated last year
- Graph Attention Auto-Encoders☆71Updated last year
- Official code for the ICML 2021 paper "Generative Causal Explanations for Graph Neural Networks."☆64Updated 2 years ago