HongtengXu / s-gwlLinks
Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching
☆44Updated 6 years ago
Alternatives and similar repositories for s-gwl
Users that are interested in s-gwl are comparing it to the libraries listed below
Sorting:
- Gromov-Wasserstein Learning for Graph Matching and Node Embedding☆73Updated 6 years ago
- Code for Optimal Transport for structured data with application on graphs☆102Updated 2 years ago
- The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).☆35Updated 4 years ago
- Gromov-Wasserstein Factorization Models for Graph Clustering (AAAI-20)☆31Updated 3 years ago
- Code for Online Graph Dictionary Learning☆18Updated 3 years ago
- Code accompanying the NeurIPS 2019 paper "GOT: An Optimal Transport framework for Graph comparison"☆43Updated 2 years ago
- Wasserstein Weisfeiler-Lehman Graph Kernels☆86Updated last year
- Graph matching and clustering by comparing heat kernels via optimal transport.☆27Updated 2 years ago
- ☆25Updated 6 years ago
- Learning Autoencoders with Relational Regularization☆46Updated 5 years ago
- Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport☆37Updated 4 years ago
- Code for Graphite iterative graph generation☆59Updated 6 years ago
- Graph meta learning via local subgraphs (NeurIPS 2020)☆126Updated last year
- The implementation of our NeurIPS 2020 paper "Graph Geometry Interaction Learning" (GIL)☆46Updated 5 years ago
- Implementation of the paper "Certifiable Robustness and Robust Training for Graph Convolutional Networks".☆43Updated 4 years ago
- ☆25Updated 4 years ago
- Official code for the ICML 2021 paper "Generative Causal Explanations for Graph Neural Networks."☆68Updated 3 years ago
- Code for reproducing results in GraphMix paper☆72Updated 3 years ago
- Source code for the "Computationally Tractable Riemannian Manifolds for Graph Embeddings" paper☆37Updated 5 years ago
- Variational Graph Convolutional Networks☆23Updated 5 years ago
- Implementation of Graph Neural Tangent Kernel (NeurIPS 2019)☆105Updated 5 years ago
- informal exposition of Weisfeiler-Leman similarity☆28Updated 4 years ago
- Python code associated with the paper "A Unifying Framework for Spectrum-Preserving Graph Sparsification and Coarsening'' (NeurIPS, 2019)☆16Updated 5 years ago
- Implementation of the Gromov-Wasserstein distance to the setting of Unbalanced Optimal Transport☆45Updated 2 years ago
- Code for "Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification"☆53Updated 5 years ago
- Generating PGM Explanation for GNN predictions☆76Updated 2 years ago
- ☆31Updated 2 years ago
- "Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data" (NeurIPS 21')☆49Updated 3 years ago
- Code for `BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery`, Neurips 2021☆26Updated 4 years ago
- ☆45Updated 8 years ago