stadlmax / Graph-Posterior-Network
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification (NeurIPS 2021)
☆41Updated 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
- The official implementation of DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks (NeurIPS 2021)☆25Updated 2 years ago
- Papers about developing DL methods on disassortative graphs☆48Updated 2 years ago
- Code for "Explainability methods for graph convolutional neural networks" - PE Pope*, S Kolouri*, M Rostami, CE Martin, H Hoffmann (CVPR …☆34Updated 5 months ago
- PyTorch code of "SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks"☆87Updated 3 years ago
- ☆46Updated 3 years ago
- This repo is for source code of NeurIPS 2021 paper "Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration".☆21Updated 3 years ago
- Pytorch implementation of "Large-Scale Representation Learning on Graphs via Bootstrapping"☆76Updated 3 years ago
- The code for the ICML 2021 paper "Graph Neural Networks Inspired by Classical Iterative Algorithms".☆43Updated 3 years ago
- MetA-Train to Explain☆17Updated 2 years ago
- Code for "Random Features Strengthen Graph Neural Networks" (SDM 2021)☆21Updated 4 years ago
- [ICLR'23] Implementation of "Empowering Graph Representation Learning with Test-Time Graph Transformation"☆56Updated last year
- Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)☆101Updated 2 years ago
- ☆25Updated 5 years ago
- Source code for PairNorm (ICLR 2020)☆76Updated 4 years ago
- PyTorch implementation of BGRL (https://arxiv.org/abs/2102.06514)☆82Updated last year
- Rex Ying's Ph.D. Thesis, Stanford University☆42Updated 2 years ago
- Gradient gating (ICLR 2023)☆53Updated last year
- Code of "Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective" paper published in ICLR2021☆45Updated 3 years ago
- ☆62Updated 4 years ago
- Official code for the ICML 2021 paper "Generative Causal Explanations for Graph Neural Networks."☆65Updated 2 years ago
- [ICLR 2022] Implementation of paper "Automated Self-Supervised Learning for Graphs"☆41Updated 2 years ago
- Variational Graph Convolutional Networks☆22Updated 4 years ago
- Official code for the CVPR 2022 (oral) paper "OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks.…☆34Updated 2 years ago
- ☆51Updated 2 years ago
- Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"☆94Updated 2 years ago
- "Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data" (NeurIPS 21')☆47Updated 2 years ago
- Bayesian Graph Neural Networks with Adaptive Connection Sampling - Pytorch☆56Updated 4 years ago
- ☆38Updated last year
- This is the official code repository for "Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs", wh…☆86Updated 7 months ago
- ☆26Updated 3 years ago