trneedham / Spectral-Gromov-WassersteinLinks
Graph matching and clustering by comparing heat kernels via optimal transport.
☆26Updated 2 years ago
Alternatives and similar repositories for Spectral-Gromov-Wasserstein
Users that are interested in Spectral-Gromov-Wasserstein 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
- Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching☆43Updated 5 years ago
- Official implementation of Joint Multidimensional Scaling☆22Updated last year
- Implementation of the Gromov-Wasserstein distance to the setting of Unbalanced Optimal Transport☆44Updated 2 years ago
- Gromov-Wasserstein Factorization Models for Graph Clustering (AAAI-20)☆31Updated 2 years ago
- Code accompanying the NeurIPS 2019 paper "GOT: An Optimal Transport framework for Graph comparison"☆41Updated last year
- Statistics on the space of asymmetric networks via Gromov-Wasserstein distance☆13Updated 5 years ago
- Gromov-Wasserstein Learning for Graph Matching and Node Embedding☆72Updated 5 years ago
- Source code for the "Computationally Tractable Riemannian Manifolds for Graph Embeddings" paper☆36Updated 5 years ago
- Embedding graphs in symmetric spaces☆30Updated 3 years ago
- Code corresponding to the paper Diffusion Earth Mover's Distance and Distribution Embeddings☆36Updated 6 months ago
- Source code for the ICML2019 paper "Subspace Robust Wasserstein Distances"☆28Updated 6 years ago
- ☆12Updated 4 years ago
- CO-Optimal Transport☆42Updated 4 years ago
- Scalable framework for comparing metric measure spaces with up to 1M points.☆14Updated 4 years ago
- Code for the KDD 2021 paper 'Filtration Curves for Graph Representation'☆18Updated last year
- ☆17Updated last year
- Code for Optimal Transport for structured data with application on graphs☆100Updated 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
- Code for computing Hypergraph Co-Optimal Transport distances☆16Updated 2 years ago
- Morgan A. Schmitz., Matthieu Heitz, Nicolas Bonneel, Fred Ngole, David Coeurjolly, Marco Cuturi, Gabriel Peyré, and Jean-Luc Starck. "Was…☆20Updated 5 years ago
- [NeurIPS 2020]. COPT - Coordinated Optimal Transport on Graphs☆16Updated 4 years ago
- ☆28Updated 3 years ago
- NeurIPS 2022: Tree Mover’s Distance: Bridging Graph Metrics and Stability of Graph Neural Networks☆37Updated last year
- Learning Generative Models across Incomparable Spaces (ICML 2019)☆27Updated 5 years ago
- Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport☆37Updated 4 years ago
- Learning Autoencoders with Relational Regularization☆46Updated 4 years ago
- GyroSPD: Vector-valued Distance and Gyrocalculus on the Space of Symmetric Positive Definite Matrices☆18Updated 3 years ago
- Python-based persistent homology algorithms☆19Updated last year
- ☆17Updated 2 years ago