Alternatives and similar repositories for gwl:

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

• HongtengXu / Relational-Factorization-Model
  Gromov-Wasserstein Factorization Models for Graph Clustering (AAAI-20)
  ☆31Updated 2 years ago
• HongtengXu / s-gwl
  Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching
  ☆43Updated 5 years ago
• tvayer / FGW
  Code for Optimal Transport for structured data with application on graphs
  ☆100Updated 2 years ago
• matenure / OTCoarsening
  Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport
  ☆36Updated 4 years ago
• navid-naderi / WEGL
  The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).
  ☆35Updated 4 years ago
• tvayer / SGW
  Code for Sliced Gromov-Wasserstein
  ☆68Updated 5 years ago
• Hermina / GOT
  Code accompanying the NeurIPS 2019 paper "GOT: An Optimal Transport framework for Graph comparison"
  ☆41Updated last year
• bluer555 / KernelGCN
  Codes for NIPS 2019 Paper: Rethinking Kernel Methods for Node Representation Learning on Graphs
  ☆34Updated 5 years ago
• vikasverma1077 / GraphMix
  Code for reproducing results in GraphMix paper
  ☆72Updated 2 years ago
• LingxiaoShawn / PairNorm
  Source code for PairNorm (ICLR 2020)
  ☆76Updated 5 years ago
• HongtengXu / Relational-AutoEncoders
  Learning Autoencoders with Relational Regularization
  ☆46Updated 4 years ago
• BorgwardtLab / WWL
  Wasserstein Weisfeiler-Lehman Graph Kernels
  ☆81Updated 8 months ago
• VoVAllen / diffpool
  ☆37Updated 6 years ago
• lchapel / partial-GW-for-PU
  Contains the code relative to the paper Partial Gromov-Wasserstein with Applications on Positive-Unlabeled Learning https://arxiv.org/abs…
  ☆21Updated 5 years ago
• claying / GCKN
  Pytorch Implementation of Graph Convolutional Kernel Networks
  ☆54Updated 2 years ago
• CUAI / Differentiable-Frechet-Mean
  [ICML 2020] Differentiating through the Fréchet Mean (https://arxiv.org/abs/2003.00335).
  ☆55Updated 3 years ago
• Shen-Lab / SS-GCNs
  [ICML 2020] "When Does Self-Supervision Help Graph Convolutional Networks?" by Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen
  ☆110Updated 3 years ago
• Nate1874 / StructPool
  The code for our ICLR paper: StructPool: Structured Graph Pooling via Conditional Random Fields
  ☆58Updated 5 years ago
• cvignac / SMP
  ☆27Updated 3 years ago
• balcilar / Spectral-Designed-Graph-Convolutions
  Codes for "Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks" paper
  ☆50Updated 4 years ago
• chentingpc / gfn
  Code for "Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification"
  ☆52Updated 5 years ago
• Haggaim / InvariantGraphNetworks
  Code for the ICLR 2019 paper "Invariant and Equiovariant Graph Networks"
  ☆24Updated 4 years ago
• KelestZ / CondGen
  Conditional Structure Generation through Graph Variational Generative Adversarial Nets, NeurIPS 2019.
  ☆54Updated 5 years ago
• HekpoMaH / algorithmic-concepts-reasoning
  ☆25Updated 3 years ago
• CheriseZhu / GIL
  The implementation of our NeurIPS 2020 paper "Graph Geometry Interaction Learning" (GIL)
  ☆45Updated 4 years ago
• ermongroup / graphite
  Code for Graphite iterative graph generation
  ☆59Updated 5 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
• chingyaoc / TMD
  NeurIPS 2022: Tree Mover’s Distance: Bridging Graph Metrics and Stability of Graph Neural Networks
  ☆36Updated last year
• jiaqima / G3NN
  A Flexible Generative Framework for Graph-based Semi-supervised Learning (NeurIPS 2019)
  ☆16Updated 3 years ago
• GentleZhu / Shift-Robust-GNNs
  "Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data" (NeurIPS 21')
  ☆48Updated 2 years ago