Alternatives and similar repositories for Hyperbolic-Embedding

Users that are interested in Hyperbolic-Embedding are comparing it to the libraries listed below

• dalab / matrix-manifolds
  Source code for the "Computationally Tractable Riemannian Manifolds for Graph Embeddings" paper
  ☆37Updated 5 years ago
• c-hofer / nips2017
  Deprecated repository for "Deep Learning with Topological Signatures"
  ☆36Updated 5 years ago
• vermaMachineLearning / FGSD
  Graph Feature Representation/Vector Based On The Family Of Graph Spectral Distances (NIPS 2017).
  ☆24Updated 5 years ago
• nd7141 / AWE
  Compute graph embeddings via Anonymous Walk Embeddings
  ☆83Updated 7 years ago
• PurdueMINDS / Equivalence
  Equivalence Between Structural Representations and Positional Node Embeddings
  ☆22Updated 5 years ago
• leowyy / GraphTSNE
  PyTorch Implementation of GraphTSNE, ICLR’19
  ☆137Updated 6 years ago
• BorgwardtLab / P-WL
  A Persistent Weisfeiler–Lehman Procedure for Graph Classification
  ☆63Updated 4 years ago
• ermongroup / graphite
  Code for Graphite iterative graph generation
  ☆59Updated 6 years ago
• nkeriven / random-graph-gnn
  ☆12Updated 4 years ago
• KangchengHou / gntk
  Implementation of Graph Neural Tangent Kernel (NeurIPS 2019)
  ☆105Updated 5 years ago
• Chen-Cai-OSU / LDP
  LDP for graph classification
  ☆23Updated 5 years ago
• emilemathieu / pvae
  code for "Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders".
  ☆128Updated 2 years ago
• crisbodnar / dgm
  Deep Graph Mapper: Seeing Graphs through the Neural Lens
  ☆58Updated 2 years ago
• google / gumbel_sinkhorn
  ☆76Updated 8 years ago
• giannisnik / cnn-graph-classification
  A convolutional neural network for graph classification in PyTorch
  ☆91Updated 6 years ago
• Cyanogenoid / perm-optim
  [ICLR 2019] Learning Representations of Sets through Optimized Permutations
  ☆36Updated 6 years ago
• PurdueMINDS / RelationalPooling
  ☆35Updated 6 years ago
• guillaumejaume / edGNN
  Code for the paper: "edGNN: A simple and powerful GNN for labeled graphs"
  ☆43Updated 2 years ago
• drewwilimitis / hyperbolic-learning
  Implemented Machine Learning Algorithms in Hyperbolic Geometry (MDS, K-Means, Support vector machines, etc.)
  ☆140Updated 5 years ago
• lateral / frechet-mean-hyperboloid
  ☆16Updated 7 years ago
• graphreason / graphreason.github.io
  Learning and Reasoning with Graph-Structured Data (ICML 2019 Workshop)
  ☆26Updated 6 years ago
• IBM / IPC-graph-data
  IPC: A Graph Data Set Compiled from International Planning Competitions
  ☆46Updated 5 years ago
• theSage21 / lorentz-embeddings
  Embed arbitrary graphs in Hyperbolic space
  ☆76Updated 3 years ago
• cshjin / gnn_in_neurips_2019
  A comprehensive collection of GNN works in NeurIPS 2019.
  ☆21Updated 5 years ago
• bluer555 / KernelGCN
  Codes for NIPS 2019 Paper: Rethinking Kernel Methods for Node Representation Learning on Graphs
  ☆34Updated 5 years ago
• nimadehmamy / Understanding-GCN
  code for the paper in NeurIPS 2019
  ☆40Updated 2 years ago
• benedekrozemberczki / GraphWaveMachine
  A scalable implementation of "Learning Structural Node Embeddings Via Diffusion Wavelets (KDD 2018)".
  ☆187Updated 2 years ago
• qiangsiwei / poincare_embedding
  Poincaré Embedding
  ☆40Updated 7 years ago
• hhcho / hyplinear
  MATLAB implementation of linear support vector classification in hyperbolic space
  ☆20Updated 7 years ago
• lttam / TreeWasserstein
  Matlab code for tree-Wasserstein distance in the paper "Tree-Sliced Variants of Wasserstein Distances", NeurIPS, 2019. (Tam Le, Makoto Y…
  ☆11Updated 5 years ago