stadlmax / Graph-Posterior-Network
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification (NeurIPS 2021)
☆42Updated 2 years ago
Alternatives and similar repositories for Graph-Posterior-Network
Users that are interested in Graph-Posterior-Network are comparing it to the libraries listed below
Sorting:
- Papers about developing DL methods on disassortative graphs☆48Updated 2 years ago
- The official implementation of DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks (NeurIPS 2021)☆25Updated 2 years ago
- ☆62Updated 4 years ago
- Rex Ying's Ph.D. Thesis, Stanford University☆42Updated 2 years ago
- Code for "Explainability methods for graph convolutional neural networks" - PE Pope*, S Kolouri*, M Rostami, CE Martin, H Hoffmann (CVPR …☆34Updated 2 months ago
- PyTorch implementation of BGRL (https://arxiv.org/abs/2102.06514)☆81Updated last year
- Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"☆94Updated 3 years ago
- Bayesian Graph Neural Networks with Adaptive Connection Sampling - Pytorch☆58Updated 4 years ago
- [ICML 2022] Local Augmentation for Graph Neural Networks☆66Updated 11 months ago
- Gradient gating (ICLR 2023)☆53Updated 2 years ago
- Source code for From Stars to Subgraphs (ICLR 2022)☆70Updated last year
- ☆46Updated 3 years ago
- ☆55Updated 3 years ago
- PyTorch code of "SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks"☆88Updated 3 years ago
- [ICLR'23] Implementation of "Empowering Graph Representation Learning with Test-Time Graph Transformation"☆56Updated last year
- [ICML 2022] pGNN, p-Laplacian Based Graph Neural Networks☆27Updated 2 years ago
- Variational Graph Convolutional Networks☆22Updated 4 years ago
- Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)☆101Updated 3 years ago
- Code of "Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective" paper published in ICLR2021☆46Updated 3 years ago
- Official code for the ICML 2021 paper "Generative Causal Explanations for Graph Neural Networks."☆66Updated 3 years ago
- "Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data" (NeurIPS 21')☆48Updated 2 years ago
- ☆41Updated last year
- The code for the ICML 2021 paper "Graph Neural Networks Inspired by Classical Iterative Algorithms".☆43Updated 3 years ago
- Code for "Random Features Strengthen Graph Neural Networks" (SDM 2021)☆22Updated 4 years ago
- ☆134Updated last year
- Code and dataset to test empirically the expressive power of graph pooling operators presented as presented at NeurIPS 2023☆37Updated last year
- How Powerful are Spectral Graph Neural Networks☆72Updated last year
- Official code for the CVPR 2022 (oral) paper "OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks.…☆34Updated 3 years ago
- Code for our paper "Attending to Graph Transformers"☆86Updated last year
- Pytorch implementation of "Large-Scale Representation Learning on Graphs via Bootstrapping"☆80Updated 3 years ago