Pangyk / Graph_AELinks
☆25Updated 3 years ago
Alternatives and similar repositories for Graph_AE
Users that are interested in Graph_AE are comparing it to the libraries listed below
Sorting:
- Source code for PairNorm (ICLR 2020)☆79Updated 5 years ago
- Wasserstein Weisfeiler-Lehman Graph Kernels☆85Updated last year
- ☆62Updated 4 years ago
- Code for "Explainability methods for graph convolutional neural networks" - PE Pope*, S Kolouri*, M Rostami, CE Martin, H Hoffmann (CVPR …☆34Updated 7 months ago
- The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).☆35Updated 4 years ago
- Pytorch Implementation of Graph Convolutional Kernel Networks☆54Updated 2 years ago
- Gromov-Wasserstein Learning for Graph Matching and Node Embedding☆72Updated 6 years ago
- Topological Graph Neural Networks (ICLR 2022)☆122Updated 3 years ago
- Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"☆97Updated 3 years ago
- Official Code Repository for the paper "Graph Ordering Attention Networks"☆21Updated last year
- D-VAE: A Variational Autoencoder for Directed Acyclic Graphs, NeurIPS 2019☆141Updated 5 years ago
- Pytorch reproduction of the paper "Gaussian Mixture Model Convolutional Networks" (CVPR 17)☆58Updated 5 years ago
- Python code associated with the paper "A Unifying Framework for Spectrum-Preserving Graph Sparsification and Coarsening'' (NeurIPS, 2019)☆16Updated 5 years ago
- Official code for the CVPR 2022 (oral) paper "OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks.…☆35Updated 3 years ago
- informal exposition of Weisfeiler-Leman similarity☆28Updated 4 years ago
- Code for Optimal Transport for structured data with application on graphs☆102Updated 2 years ago
- Geo2DR: A Python and PyTorch library for learning distributed representations of graphs.☆46Updated 2 years ago
- PyTorch implementation of "Graph Convolutional Networks for Graphs Containing Missing Features"☆48Updated last year
- Official code for the ICML 2021 paper "Generative Causal Explanations for Graph Neural Networks."☆68Updated 3 years ago
- Implementation of Directional Graph Networks in PyTorch and DGL☆117Updated 4 years ago
- Implementation of "Explainability Methods for Graph Convolutional Neural Networks" from HRL Laboratories☆84Updated 3 years ago
- ☆31Updated 2 years ago
- Random Walk Graph Neural Networks☆54Updated 4 years ago
- Gradient gating (ICLR 2023)☆53Updated 2 years ago
- Codes for "Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks" paper☆50Updated 4 years ago
- Bayesian Graph Neural Networks with Adaptive Connection Sampling - Pytorch☆61Updated 5 years ago
- ☆25Updated 4 years ago
- Conditional Structure Generation through Graph Variational Generative Adversarial Nets, NeurIPS 2019.☆54Updated 6 years ago
- ☆44Updated 8 years ago
- Python package for graph-based clustering and semi-supervised learning☆99Updated last month