twistedcubic / coptLinks
[NeurIPS 2020]. COPT - Coordinated Optimal Transport on Graphs
☆16Updated 4 years ago
Alternatives and similar repositories for copt
Users that are interested in copt are comparing it to the libraries listed below
Sorting:
- Code accompanying the NeurIPS 2019 paper "GOT: An Optimal Transport framework for Graph comparison"☆41Updated last year
- The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).☆35Updated 4 years ago
- Gromov-Wasserstein Learning for Graph Matching and Node Embedding☆72Updated 5 years ago
- Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport☆37Updated 4 years ago
- CO-Optimal Transport☆42Updated 4 years ago
- Uncertainty Aware Semi-Supervised Learning on Graph Data☆40Updated 4 years ago
- Graph matching and clustering by comparing heat kernels via optimal transport.☆26Updated 2 years ago
- Python code associated with the paper "A Unifying Framework for Spectrum-Preserving Graph Sparsification and Coarsening'' (NeurIPS, 2019)☆16Updated 5 years ago
- Gromov-Wasserstein Factorization Models for Graph Clustering (AAAI-20)☆31Updated 2 years ago
- Source code for the ICML2019 paper "Subspace Robust Wasserstein Distances"☆28Updated 6 years ago
- Code for Graph Neural Networks Exponentially Lose Expressive Power for Node Classification.☆30Updated 5 years ago
- Implementation of the Gromov-Wasserstein distance to the setting of Unbalanced Optimal Transport☆44Updated 2 years ago
- Codes for "Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks" paper☆50Updated 4 years ago
- [ICML 2020] Differentiating through the Fréchet Mean (https://arxiv.org/abs/2003.00335).☆56Updated 3 years ago
- Source code for the "Computationally Tractable Riemannian Manifolds for Graph Embeddings" paper☆36Updated 5 years ago
- Learning Graphons via Structured Gromov-Wasserstein Barycenters☆22Updated 4 years ago
- ☆21Updated 3 years ago
- NeurIPS 2022: Tree Mover’s Distance: Bridging Graph Metrics and Stability of Graph Neural Networks☆37Updated last year
- The source code of the paper "Understanding Graph Neural Networks from Graph Signal Denoising Perspectives"☆23Updated 5 years ago
- Contains the code relative to the paper Partial Gromov-Wasserstein with Applications on Positive-Unlabeled Learning https://arxiv.org/abs…☆21Updated 5 years ago
- The code for the ICML 2021 paper "Graph Neural Networks Inspired by Classical Iterative Algorithms".☆43Updated 4 years ago
- ☆17Updated 2 years ago
- A Flexible Generative Framework for Graph-based Semi-supervised Learning (NeurIPS 2019)☆16Updated 3 years ago
- informal exposition of Weisfeiler-Leman similarity☆28Updated 4 years ago
- Learning Autoencoders with Relational Regularization☆46Updated 4 years ago
- Spectral Graph Attention Network with Fast Eigen-approximation☆12Updated 3 years ago
- ☆24Updated 3 years ago
- Rethinking Graph Regularization for Graph Neural Networks (AAAI2021)☆34Updated 4 years ago
- Code for “ACE-HGNN: Adaptive Curvature ExplorationHyperbolic Graph Neural Network”☆15Updated 3 years ago
- Variational Graph Convolutional Networks☆23Updated 4 years ago