jliu / graph-normalizing-flowsLinks
Code for Graph Normalizing Flows.
☆63Updated 5 years ago
Alternatives and similar repositories for graph-normalizing-flows
Users that are interested in graph-normalizing-flows are comparing it to the libraries listed below
Sorting:
- Official implementation for the paper: Permutation Invariant Graph Generation via Score-Based Generative Modeling☆111Updated 2 years ago
- Code for the ICLR 2019 paper "Invariant and Equiovariant Graph Networks"☆24Updated 5 years ago
- Official repository for "Categorical Normalizing Flows via Continuous Transformations"☆57Updated 4 years ago
- Source code for the "Computationally Tractable Riemannian Manifolds for Graph Embeddings" paper☆36Updated 5 years ago
- GraphCON (ICML 2022)☆59Updated 2 years ago
- Code for Graphite iterative graph generation☆59Updated 6 years ago
- ☆28Updated 3 years ago
- SignNet and BasisNet☆101Updated 2 years ago
- Implementation of Graph Neural Tangent Kernel (NeurIPS 2019)☆105Updated 5 years ago
- Equivariant Subgraph Aggregation Networks (ICLR 2022 Spotlight)☆89Updated 2 years ago
- Code accompanying the NeurIPS 2019 paper "GOT: An Optimal Transport framework for Graph comparison"☆41Updated last year
- ☆38Updated 5 years ago
- ☆44Updated 7 years ago
- Mixed-curvature Variational Autoencoders (ICLR 2020)☆62Updated 4 years ago
- Code for Optimal Transport for structured data with application on graphs☆101Updated 2 years ago
- code for "Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders".☆126Updated last year
- Implementation of Directional Graph Networks in PyTorch and DGL☆118Updated 4 years ago
- Repository for Autobahn: Automorphism Based Graph Neural Networks☆30Updated 3 years ago
- Source code for PairNorm (ICLR 2020)☆79Updated 5 years ago
- SetToGraph paper repository☆22Updated 4 years ago
- Expressive Power of Invariant and Equivariant Graph Neural Networks (ICLR 2021)☆40Updated last year
- Codes for "Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks" paper☆50Updated 4 years ago
- ☆62Updated 4 years ago
- [NeurIPS'21] Higher-order Transformers for sets, graphs, and hypergraphs, in PyTorch☆68Updated 2 years ago
- ☆40Updated 3 years ago
- Deep generative modeling for time-stamped heterogeneous data, enabling high-fidelity models for a large variety of spatio-temporal domain…☆106Updated 3 years ago
- ICML 2020 Paper: Latent Variable Modelling with Hyperbolic Normalizing Flows☆54Updated 2 years ago
- ☆56Updated 3 years ago
- Reference implementation for SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators (ICML …☆26Updated 2 years ago
- GraphNVP: An Invertible Flow Model for Generating Molecular Graphs☆95Updated 3 years ago