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:
- The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).☆35Updated 4 years ago
- Uncertainty Aware Semi-Supervised Learning on Graph Data☆38Updated 4 years ago
- Source code for the ICML2019 paper "Subspace Robust Wasserstein Distances"☆29Updated 6 years ago
- Codes for "Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks" paper☆50Updated 4 years ago
- informal exposition of Weisfeiler-Leman similarity☆28Updated 4 years ago
- ☆17Updated 3 years ago
- Graph matching and clustering by comparing heat kernels via optimal transport.☆27Updated 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
- Source code for the "Computationally Tractable Riemannian Manifolds for Graph Embeddings" paper☆37Updated 5 years ago
- Stochastic algorithms for computing Regularized Optimal Transport☆58Updated 7 years ago
- Code accompanying the NeurIPS 2019 paper "GOT: An Optimal Transport framework for Graph comparison"☆43Updated last year
- CO-Optimal Transport☆42Updated 4 years ago
- NeurIPS 2022: Tree Mover’s Distance: Bridging Graph Metrics and Stability of Graph Neural Networks☆37Updated 2 years ago
- Gromov-Wasserstein Learning for Graph Matching and Node Embedding☆72Updated 6 years ago
- Implementation of the Gromov-Wasserstein distance to the setting of Unbalanced Optimal Transport☆45Updated 2 years ago
- code for the paper in NeurIPS 2019☆40Updated 2 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
- The implementation of our NeurIPS 2020 paper "Graph Geometry Interaction Learning" (GIL)☆46Updated 4 years ago
- Implicit Graph Neural Networks☆64Updated 3 years ago
- [ICML 2020] Differentiating through the Fréchet Mean (https://arxiv.org/abs/2003.00335).☆59Updated 4 years ago
- Code for ECML/PKDD paper: "LSMI-Sinkhorn: Semi-supervised Mutual Information Estimation with Optimal Transport"☆16Updated 4 years ago
- Contains the code relative to the paper Partial Gromov-Wasserstein with Applications on Positive-Unlabeled Learning https://arxiv.org/abs…☆20Updated 5 years ago
- Statistics on the space of asymmetric networks via Gromov-Wasserstein distance☆15Updated 5 years ago
- Wasserstein Weisfeiler-Lehman Graph Kernels☆85Updated last year
- ☆30Updated 2 years ago
- Code of "Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective" paper published in ICLR2021☆46Updated 4 years ago
- ☆12Updated 7 years ago
- Gromov-Wasserstein Factorization Models for Graph Clustering (AAAI-20)☆31Updated 3 years ago
- Code for the KDD 2021 paper 'Filtration Curves for Graph Representation'☆18Updated 2 years ago
- Learning Autoencoders with Relational Regularization☆46Updated 5 years ago