HongtengXu / s-gwl
Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching
☆40Updated 5 years ago
Related projects: ⓘ
- Gromov-Wasserstein Learning for Graph Matching and Node Embedding☆70Updated 5 years ago
- The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).☆30Updated 3 years ago
- Code for Online Graph Dictionary Learning☆15Updated 2 years ago
- Python code associated with the paper "A Unifying Framework for Spectrum-Preserving Graph Sparsification and Coarsening'' (NeurIPS, 2019)☆15Updated 4 years ago
- Code accompanying the NeurIPS 2019 paper "GOT: An Optimal Transport framework for Graph comparison"☆38Updated 10 months ago
- Gromov-Wasserstein Factorization Models for Graph Clustering (AAAI-20)☆30Updated last year
- Wasserstein Weisfeiler-Lehman Graph Kernels☆77Updated 2 weeks ago
- Graph matching and clustering by comparing heat kernels via optimal transport.☆23Updated last year
- ☆25Updated 2 years ago
- Variational Graph Convolutional Networks☆20Updated 3 years ago
- Code for Optimal Transport for structured data with application on graphs☆95Updated last year
- Code for the ICLR 2019 paper "Invariant and Equiovariant Graph Networks"☆23Updated 4 years ago
- "Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data" (NeurIPS 21')☆47Updated 2 years ago
- ☆24Updated 3 years ago
- Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport☆36Updated 3 years ago
- Code for `BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery`, Neurips 2021☆24Updated 2 years ago
- Pytorch Implementation of Graph Convolutional Kernel Networks☆53Updated last year
- ☆30Updated last year
- Code for Graphite iterative graph generation☆56Updated 5 years ago
- Code for the KDD 2021 paper 'Filtration Curves for Graph Representation'☆18Updated last year
- Source code for the "Computationally Tractable Riemannian Manifolds for Graph Embeddings" paper☆35Updated 4 years ago
- Official implementation for the paper: Permutation Invariant Graph Generation via Score-Based Generative Modeling☆104Updated last year
- Official implementation of the ICML2021 paper "Elastic Graph Neural Networks"☆40Updated 3 years ago
- ☆25Updated 4 years ago
- A list of papers for group meeting☆15Updated last week
- Lorentzian Distance Learning for Hyperbolic Representations: Retrieval experiments☆11Updated 5 years ago
- Conditional Structure Generation through Graph Variational Generative Adversarial Nets, NeurIPS 2019.☆53Updated 5 years ago
- Code for "Weisfeiler and Leman go sparse: Towards higher-order graph embeddings"☆22Updated 2 years ago
- Official repository for the paper "On Evaluation Metrics for Graph Generative Models"☆26Updated 2 years ago
- Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"☆91Updated 2 years ago