JunwenBai / correlation-gnnLinks
Correlated Graph Neural Networks
☆27Updated 5 years ago
Alternatives and similar repositories for correlation-gnn
Users that are interested in correlation-gnn are comparing it to the libraries listed below
Sorting:
- ☆20Updated 4 years ago
- The official implementation of the Graph Barlow Twins method with the experimental pipeline☆31Updated last year
- Code and dataset to test empirically the expressive power of graph pooling operators presented as presented at NeurIPS 2023☆37Updated last year
- Bayesian Graph Neural Networks with Adaptive Connection Sampling - Pytorch☆60Updated 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
- Dataset for testing graph classification algorithms, such as Graph Kernels and Graph Neural Networks.☆49Updated 3 months ago
- ☆28Updated 3 years ago
- ☆62Updated 4 years ago
- ☆18Updated last year
- Pytorch Implementation of Graph Convolutional Kernel Networks☆54Updated 2 years ago
- Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"☆94Updated 3 years ago
- Equivariant Subgraph Aggregation Networks (ICLR 2022 Spotlight)☆89Updated 2 years ago
- Deep generative modeling for time-stamped heterogeneous data, enabling high-fidelity models for a large variety of spatio-temporal domain…☆105Updated 3 years ago
- ☆82Updated 3 years ago
- Implementation of Graph Neural Tangent Kernel (NeurIPS 2019)☆104Updated 5 years ago
- GraphCON (ICML 2022)☆59Updated 2 years ago
- The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).☆35Updated 4 years ago
- Code for Graph Normalizing Flows.☆62Updated 5 years ago
- PyTorch implementation of Pseudo-Riemannian Graph Convolutional Networks (NeurIPS'22))☆16Updated 11 months ago
- A Note On Over-Smoothing for Graph Neural Network☆20Updated 4 years ago
- Gradient gating (ICLR 2023)☆53Updated 2 years ago
- ☆47Updated 3 years ago
- Source code for the "Computationally Tractable Riemannian Manifolds for Graph Embeddings" paper☆36Updated 5 years ago
- Source code for PairNorm (ICLR 2020)☆78Updated 5 years ago
- PyTorch code of "SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks"☆88Updated 3 years ago
- Official code for the ICML 2021 paper "Generative Causal Explanations for Graph Neural Networks."☆66Updated 3 years ago
- The official implementation of DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks (NeurIPS 2021)☆26Updated 3 years ago
- A Graph Structure Learning (GSL) Toolkit☆36Updated 2 years ago
- Generating PGM Explanation for GNN predictions☆75Updated last year
- Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)☆103Updated last week