uestc-lese / SPGRLLinks
☆17Updated 2 years ago
Alternatives and similar repositories for SPGRL
Users that are interested in SPGRL are comparing it to the libraries listed below
Sorting:
- ☆12Updated 4 years ago
- This repository summarises the open source codes of our group☆27Updated 2 years ago
- ☆16Updated 5 years ago
- ☆11Updated 11 months ago
- Scattering GCN: overcoming oversmoothness in graph convolutional networks☆26Updated 3 years ago
- Graph matching and clustering by comparing heat kernels via optimal transport.☆26Updated 2 years ago
- The implementation of our AAAI 2020 paper "GSSNN: Graph Smoothing Splines Neural Network".☆20Updated 4 years ago
- A PyTorch implementation of "SelfGNN: Self-supervised Graph Neural Networks without explicit negative sampling" paper, which appeared in …☆30Updated 3 years ago
- ☆31Updated 2 years ago
- The PyTorch implementation of Directed Graph Contrastive Learning (DiGCL), NeurIPS-2021☆36Updated 3 years ago
- Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport☆37Updated 4 years ago
- The source will be uploaded recently☆11Updated 4 years ago
- The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).☆35Updated 4 years ago
- ☆12Updated 3 years ago
- Official implementation of NeurIPS'21: Implicit SVD for Graph Representation Learning☆20Updated 3 years ago
- The source code of the paper "Understanding Graph Neural Networks from Graph Signal Denoising Perspectives"☆22Updated 5 years ago
- Official PyTorch implementation of "Towards Deeper Graph Neural Networks" [KDD2020]☆69Updated 2 years ago
- IJCAI 2022- Escaping Feature Twist: A Variational Graph Auto-Encoder for Node Clustering☆21Updated 2 years ago
- PyTorch Codes for Haar Graph Pooling☆11Updated 2 years ago
- Graph Transformers for Large Graphs☆21Updated last year
- Source code for "Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation" (IJCAI 2020)☆17Updated 11 months ago
- Variational Graph Convolutional Networks☆23Updated 4 years ago
- Code for Graph Neural Networks Exponentially Lose Expressive Power for Node Classification.☆30Updated 5 years ago
- Source code for the "Computationally Tractable Riemannian Manifolds for Graph Embeddings" paper☆36Updated 5 years ago
- ☆4Updated 2 years ago
- ☆19Updated 3 years ago
- Official implementation of the ICML2021 paper "Elastic Graph Neural Networks"☆42Updated 3 years ago
- ☆15Updated 2 years ago
- ☆12Updated 5 years ago
- Official implementation of the ICML 2022 paper "Going Deeper into Permutation-Sensitive Graph Neural Networks"☆27Updated 3 years ago