Alternatives and similar repositories for matrix-manifolds

Users that are interested in matrix-manifolds are comparing it to the libraries listed below

• nkeriven / random-graph-gnn
  ☆12Updated 4 years ago
• trneedham / Spectral-Gromov-Wasserstein
  Graph matching and clustering by comparing heat kernels via optimal transport.
  ☆26Updated 2 years ago
• KangchengHou / gntk
  Implementation of Graph Neural Tangent Kernel (NeurIPS 2019)
  ☆104Updated 5 years ago
• nimadehmamy / Understanding-GCN
  code for the paper in NeurIPS 2019
  ☆40Updated 2 years ago
• bluer555 / KernelGCN
  Codes for NIPS 2019 Paper: Rethinking Kernel Methods for Node Representation Learning on Graphs
  ☆34Updated 5 years ago
• samihaija / isvd
  Official implementation of NeurIPS'21: Implicit SVD for Graph Representation Learning
  ☆20Updated 3 years ago
• Haggaim / InvariantGraphNetworks
  Code for the ICLR 2019 paper "Invariant and Equiovariant Graph Networks"
  ☆24Updated 5 years ago
• CUAI / Differentiable-Frechet-Mean
  [ICML 2020] Differentiating through the Fréchet Mean (https://arxiv.org/abs/2003.00335).
  ☆56Updated 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
• hadarser / SetToGraphPaper
  SetToGraph paper repository
  ☆22Updated 4 years ago
• karl-zhao / benchmarking-gnns-pyg
  ☆31Updated 2 years ago
• jliu / graph-normalizing-flows
  Code for Graph Normalizing Flows.
  ☆62Updated 5 years ago
• chingyaoc / TMD
  NeurIPS 2022: Tree Mover’s Distance: Bridging Graph Metrics and Stability of Graph Neural Networks
  ☆37Updated last year
• cvignac / SMP
  ☆28Updated 3 years ago
• francoispierrepaty / SubspaceRobustWasserstein
  Source code for the ICML2019 paper "Subspace Robust Wasserstein Distances"
  ☆29Updated 6 years ago
• ermongroup / graphite
  Code for Graphite iterative graph generation
  ☆59Updated 6 years ago
• mblondel / smooth-ot
  Python implementation of smooth optimal transport.
  ☆58Updated 4 years ago
• PurdueMINDS / RelationalPooling
  ☆35Updated 6 years ago
• delta2323 / gnn-asymptotics
  Code for Graph Neural Networks Exponentially Lose Expressive Power for Node Classification.
  ☆30Updated 5 years ago
• jacobdeasy / geometric-js
  ☆16Updated 4 years ago
• dimkal89 / rvae
  ☆38Updated 5 years ago
• shchur / triangular-tpp
  Implementation of "Fast and Flexible Temporal Point Processes with Triangular Maps" (Oral @ NeurIPS 2020)
  ☆23Updated last year
• navid-naderi / WEGL
  The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).
  ☆35Updated 4 years ago
• claying / GCKN
  Pytorch Implementation of Graph Convolutional Kernel Networks
  ☆54Updated 2 years ago
• joeybose / HyperbolicNF
  ICML 2020 Paper: Latent Variable Modelling with Hyperbolic Normalizing Flows
  ☆54Updated 2 years ago
• NNReasoning / What-Can-Neural-Networks-Reason-About
  ☆44Updated 5 years ago
• MarcTLaw / UltrahyperbolicRepresentation
  Ultrahyperbolic Representation Learning
  ☆13Updated 5 years ago
• BorgwardtLab / filtration_curves
  Code for the KDD 2021 paper 'Filtration Curves for Graph Representation'
  ☆18Updated last year
• HekpoMaH / algorithmic-concepts-reasoning
  ☆25Updated 3 years ago
• oskopek / mvae
  Mixed-curvature Variational Autoencoders (ICLR 2020)
  ☆62Updated 4 years ago