xpuoxford / L2G-neurips2021Links
☆26Updated 3 years ago
Alternatives and similar repositories for L2G-neurips2021
Users that are interested in L2G-neurips2021 are comparing it to the libraries listed below
Sorting:
- Variational Graph Convolutional Networks☆23Updated 4 years ago
- Code of "Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective" paper published in ICLR2021☆46Updated 4 years ago
- The official implementation of DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks (NeurIPS 2021)☆26Updated 3 years ago
- Code for Neural Relational Inference with Efficient Message Passing Mechanisms (AAAI 2021).☆17Updated 4 years ago
- Source code for NeurIPS 2019 paper "Learning Latent Processes from High-Dimensional Event Sequences via Efficient Sampling""☆10Updated 4 years ago
- ☆47Updated 3 years ago
- Source code for PairNorm (ICLR 2020)☆78Updated 5 years ago
- Neural Dynamics on Complex Networks☆53Updated 4 years ago
- NeurIPS 2021 paper 'Representation Learning on Spatial Networks' code☆18Updated 3 years ago
- Reinforced Causal Explainer for Graph Neural Networks, TPAMI2022☆36Updated 3 years ago
- ☆25Updated last year
- Official code for the ICML 2021 paper "Generative Causal Explanations for Graph Neural Networks."☆66Updated 3 years ago
- Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification (NeurIPS 2021)☆43Updated 2 years ago
- ☆50Updated 2 years ago
- ☆45Updated last year
- ☆18Updated 4 years ago
- ☆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 3 years ago
- ☆13Updated 6 months ago
- Bayesian Graph Neural Networks with Adaptive Connection Sampling - Pytorch☆60Updated 4 years ago
- ☆100Updated last year
- The PyTorch implementation of Directed Graph Contrastive Learning (DiGCL), NeurIPS-2021☆36Updated 3 years ago
- Official Repository of "Graph Mixture Density Networks" (ICML 2021)☆26Updated 2 years ago
- [ICML 2022] pGNN, p-Laplacian Based Graph Neural Networks☆27Updated 2 years ago
- ☆36Updated 3 years ago
- Code for Graph Neural Networks Exponentially Lose Expressive Power for Node Classification.☆30Updated 5 years ago
- ☆57Updated 3 years ago
- Uncertainty Quantification over Graph with Conformalized Graph Neural Networks (NeurIPS 2023)☆81Updated last year
- Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)☆103Updated last week
- ☆28Updated 3 years ago