Alternatives and similar repositories for CurvGN

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

• HongtengXu / gwl
  Gromov-Wasserstein Learning for Graph Matching and Node Embedding
  ☆72Updated 5 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
• 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
  ☆37Updated 4 years ago
• chrsmrrs / sparsewl
  Code for "Weisfeiler and Leman go sparse: Towards higher-order graph embeddings"
  ☆22Updated 3 years ago
• tech-srl / bottleneck
  Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"
  ☆94Updated 3 years ago
• Nate1874 / StructPool
  The code for our ICLR paper: StructPool: Structured Graph Pooling via Conditional Random Fields
  ☆58Updated 5 years ago
• PurdueMINDS / Equivalence
  Equivalence Between Structural Representations and Positional Node Embeddings
  ☆22Updated 5 years ago
• vikasverma1077 / GraphMix
  Code for reproducing results in GraphMix paper
  ☆72Updated 2 years ago
• dalab / matrix-manifolds
  Source code for the "Computationally Tractable Riemannian Manifolds for Graph Embeddings" paper
  ☆36Updated 4 years ago
• nimadehmamy / Understanding-GCN
  code for the paper in NeurIPS 2019
  ☆40Updated 2 years ago
• BorgwardtLab / WWL
  Wasserstein Weisfeiler-Lehman Graph Kernels
  ☆81Updated 9 months ago
• navid-naderi / WEGL
  The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).
  ☆35Updated 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
• gasteigerjo / gdc
  Graph Diffusion Convolution, as proposed in "Diffusion Improves Graph Learning" (NeurIPS 2019)
  ☆272Updated 2 years ago
• twistedcubic / HNHN
  Hypergraph representation learning: Hypergraph Networks with Hyperedge Neurons.
  ☆41Updated 4 years ago
• cvignac / SMP
  ☆28Updated 3 years ago
• karl-zhao / benchmarking-gnns-pyg
  ☆31Updated 2 years ago
• HongtengXu / Relational-Factorization-Model
  Gromov-Wasserstein Factorization Models for Graph Clustering (AAAI-20)
  ☆31Updated 2 years ago
• KelestZ / CondGen
  Conditional Structure Generation through Graph Variational Generative Adversarial Nets, NeurIPS 2019.
  ☆54Updated 5 years ago
• LingxiaoShawn / PairNorm
  Source code for PairNorm (ICLR 2020)
  ☆78Updated 5 years ago
• BorgwardtLab / filtration_curves
  Code for the KDD 2021 paper 'Filtration Curves for Graph Representation'
  ☆18Updated last year
• bluer555 / KernelGCN
  Codes for NIPS 2019 Paper: Rethinking Kernel Methods for Node Representation Learning on Graphs
  ☆34Updated 5 years ago
• Haggaim / InvariantGraphNetworks
  Code for the ICLR 2019 paper "Invariant and Equiovariant Graph Networks"
  ☆24Updated 5 years ago
• HongtengXu / s-gwl
  Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching
  ☆43Updated 5 years ago
• stadlmax / Graph-Posterior-Network
  Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification (NeurIPS 2021)
  ☆43Updated 2 years ago
• DeepGraphLearning / ContinuousGNN
  ☆47Updated 3 years ago
• gear / gfnn
  Graph Filter Neural Network (ICPR'20)
  ☆48Updated 4 years ago
• tk-rusch / gradientgating
  Gradient gating (ICLR 2023)
  ☆53Updated 2 years ago
• chentingpc / gfn
  Code for "Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification"
  ☆52Updated 5 years ago