stadlmax / Graph-Posterior-NetworkLinks
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification (NeurIPS 2021)
☆43Updated 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:
- Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)☆104Updated last month
- Papers about developing DL methods on disassortative graphs☆48Updated 3 years ago
- Code of "Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective" paper published in ICLR2021☆46Updated 4 years ago
- PyTorch code of "SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks"☆88Updated 3 years ago
- [ICLR'22] [KDD'22] [IJCAI'24] Implementation of "Graph Condensation for Graph Neural Networks"☆140Updated 9 months ago
- Graph Structured Neural Network☆40Updated 2 years ago
- ☆47Updated 3 years ago
- ☆62Updated 4 years ago
- Bayesian Graph Neural Networks with Adaptive Connection Sampling - Pytorch☆61Updated 4 years ago
- Graph Information Bottleneck (GIB) for learning minimal sufficient structural and feature information using GNNs☆137Updated 2 years ago
- The official implementation of DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks (NeurIPS 2021)☆26Updated 3 years ago
- [ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.☆170Updated last year
- ☆135Updated 2 years ago
- [ICML 2022] Local Augmentation for Graph Neural Networks☆65Updated last year
- Codes for "Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks"☆38Updated 2 years ago
- ☆29Updated 3 years ago
- [ICML 2022] pGNN, p-Laplacian Based Graph Neural Networks☆27Updated 3 years ago
- Towards Multi-Grained Explainability for Graph Neural Networks (NeurIPS 2021) + Pytorch Implementation of GNN attribution methods☆69Updated 5 months ago
- [NeurIPS 2022] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs☆116Updated last year
- This repo is for source code of NeurIPS 2021 paper "Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration".☆22Updated 3 years ago
- The code for the ICML 2021 paper "Graph Neural Networks Inspired by Classical Iterative Algorithms".☆43Updated 4 years ago
- This is the official code repository for "Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs", wh…☆88Updated last year
- Variational Graph Convolutional Networks☆23Updated 4 years ago
- [ICLR'23] Implementation of "Empowering Graph Representation Learning with Test-Time Graph Transformation"☆60Updated 2 years ago
- Code of "Breaking the Limits of Message Passing Graph Neural Networks" paper published in ICML2021☆41Updated 4 years ago
- MagNet graph convolutional network☆40Updated last year
- Code for "Random Features Strengthen Graph Neural Networks" (SDM 2021)☆22Updated 4 years ago
- Rex Ying's Ph.D. Thesis, Stanford University☆41Updated 3 years ago
- Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"☆95Updated 3 years ago
- GOOD: A Graph Out-of-Distribution Benchmark [NeurIPS 2022 Datasets and Benchmarks]☆200Updated 5 months ago