Alternatives and similar repositories for s-gwl:

Users that are interested in s-gwl are comparing it to the libraries listed below

• HongtengXu / gwl
  Gromov-Wasserstein Learning for Graph Matching and Node Embedding
  ☆72Updated 5 years ago
• HongtengXu / Relational-Factorization-Model
  Gromov-Wasserstein Factorization Models for Graph Clustering (AAAI-20)
  ☆31Updated 2 years ago
• cedricvincentcuaz / GDL
  Code for Online Graph Dictionary Learning
  ☆15Updated 3 years ago
• matenure / OTCoarsening
  Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport
  ☆36Updated 4 years ago
• ebonilla / VGCN
  Variational Graph Convolutional Networks
  ☆22Updated 4 years ago
• BorgwardtLab / WWL
  Wasserstein Weisfeiler-Lehman Graph Kernels
  ☆81Updated 6 months ago
• navid-naderi / WEGL
  The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).
  ☆33Updated 4 years ago
• TheGravLab / A-Unifying-Framework-for-Spectrum-Preserving-Graph-Sparsification-and-Coarsening
  Python code associated with the paper "A Unifying Framework for Spectrum-Preserving Graph Sparsification and Coarsening'' (NeurIPS, 2019)
  ☆16Updated 4 years ago
• Hermina / GOT
  Code accompanying the NeurIPS 2019 paper "GOT: An Optimal Transport framework for Graph comparison"
  ☆41Updated last year
• trneedham / Spectral-Gromov-Wasserstein
  Graph matching and clustering by comparing heat kernels via optimal transport.
  ☆26Updated 2 years ago
• mtang724 / NWR-GAE
  ☆38Updated 2 years ago
• GentleZhu / Shift-Robust-GNNs
  "Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data" (NeurIPS 21')
  ☆47Updated 2 years ago
• BorealisAI / SLAPS-GNN
  PyTorch code of "SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks"
  ☆87Updated 3 years ago
• cvignac / SMP
  ☆26Updated 3 years ago
• balcilar / gnn-spectral-expressive-power
  Code of "Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective" paper published in ICLR2021
  ☆46Updated 3 years ago
• CheriseZhu / GIL
  The implementation of our NeurIPS 2020 paper "Graph Geometry Interaction Learning" (GIL)
  ☆45Updated 4 years ago
• xiyou3368 / DGVAE
  ☆24Updated 3 years ago
• tvayer / FGW
  Code for Optimal Transport for structured data with application on graphs
  ☆98Updated 2 years ago
• guoji-fu / pGNNs
  [ICML 2022] pGNN, p-Laplacian Based Graph Neural Networks
  ☆27Updated 2 years ago
• GraphFramEx / graphframex
  GraphFramEx: a systematic evaluation framework for explainability methods on GNNs
  ☆43Updated 11 months ago
• sigvae / SIGraphVAE
  ☆25Updated 5 years ago
• ispamm / MATE
  MetA-Train to Explain
  ☆17Updated 3 years ago
• FFTYYY / TWIRLS
  The code for the ICML 2021 paper "Graph Neural Networks Inspired by Classical Iterative Algorithms".
  ☆43Updated 3 years ago
• flyingtango / DiGCL
  The PyTorch implementation of Directed Graph Contrastive Learning (DiGCL), NeurIPS-2021
  ☆36Updated 3 years ago
• ermongroup / graphite
  Code for Graphite iterative graph generation
  ☆59Updated 5 years ago
• stadlmax / Graph-Posterior-Network
  Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification (NeurIPS 2021)
  ☆41Updated 2 years ago
• DeepGraphLearning / SPN
  ☆29Updated 2 years ago
• Namkyeong / BGRL_Pytorch
  Pytorch implementation of "Large-Scale Representation Learning on Graphs via Bootstrapping"
  ☆78Updated 3 years ago
• ahxt / G2R
  [WWW2022] Geometric Graph Representation Learning via Maximizing Rate Reduction
  ☆26Updated 2 years ago
• pbielak / graph-barlow-twins
  The official implementation of the Graph Barlow Twins method with the experimental pipeline
  ☆30Updated last year