Alternatives and similar repositories for CGMM

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

• NNReasoning / What-Can-Neural-Networks-Reason-About
  ☆44Updated 5 years ago
• KangchengHou / gntk
  Implementation of Graph Neural Tangent Kernel (NeurIPS 2019)
  ☆105Updated 5 years ago
• xbresson / spatial_graph_convnets
  PyTorch implementation of residual gated graph ConvNets, ICLR’18
  ☆124Updated 6 years ago
• ML-on-structures / graph-lstm
  LSTM implementation, and multi-layer LSTMs for learning on graph neighborhoods
  ☆80Updated 8 years ago
• nimadehmamy / Understanding-GCN
  code for the paper in NeurIPS 2019
  ☆40Updated 2 years ago
• leowyy / GraphTSNE
  PyTorch Implementation of GraphTSNE, ICLR’19
  ☆137Updated 6 years ago
• matenure / OTCoarsening
  Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport
  ☆37Updated 4 years ago
• giannisnik / cnn-graph-classification
  A convolutional neural network for graph classification in PyTorch
  ☆91Updated 6 years ago
• muhanzhang / D-VAE
  D-VAE: A Variational Autoencoder for Directed Acyclic Graphs, NeurIPS 2019
  ☆141Updated 5 years ago
• delta2323 / gnn-asymptotics
  Code for Graph Neural Networks Exponentially Lose Expressive Power for Node Classification.
  ☆30Updated 5 years ago
• facebookresearch / hgnn
  Hyperbolic Graph Neural Networks
  ☆240Updated 5 years ago
• IBM / IPC-graph-data
  IPC: A Graph Data Set Compiled from International Planning Competitions
  ☆46Updated 5 years ago
• Hanjun-Dai / steady_state_embedding
  Learning Steady-States of Iterative Algorithms over Graphs
  ☆40Updated 7 years ago
• PurdueMINDS / RelationalPooling
  ☆35Updated 6 years ago
• bluer555 / KernelGCN
  Codes for NIPS 2019 Paper: Rethinking Kernel Methods for Node Representation Learning on Graphs
  ☆34Updated 5 years ago
• amirkhas / GraphMemoryNet
  Memory-Based Graph Networks
  ☆104Updated 3 years ago
• shchur / triangular-tpp
  Implementation of "Fast and Flexible Temporal Point Processes with Triangular Maps" (Oral @ NeurIPS 2020)
  ☆23Updated last year
• deezer / linear_graph_autoencoders
  Source code from the NeurIPS 2019 workshop article "Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks" (G. Salha, R…
  ☆134Updated 4 years ago
• chentingpc / gfn
  Code for "Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification"
  ☆53Updated 5 years ago
• bwilder0 / clusternet
  Code release for NeurIPS 2019 paper "End to End Learning and Optimization on Graphs"
  ☆91Updated 5 years ago
• Cyanogenoid / fspool
  [ICLR 2020] FSPool: Learning Set Representations with Featurewise Sort Pooling
  ☆42Updated 2 years ago
• Cyanogenoid / perm-optim
  [ICLR 2019] Learning Representations of Sets through Optimized Permutations
  ☆36Updated 6 years ago
• KelestZ / CondGen
  Conditional Structure Generation through Graph Variational Generative Adversarial Nets, NeurIPS 2019.
  ☆54Updated 6 years ago
• nke001 / causal_learning_unknown_interventions
  Code for "Neural causal learning from unknown interventions"
  ☆104Updated 5 years ago
• LingxiaoShawn / PairNorm
  Source code for PairNorm (ICLR 2020)
  ☆79Updated 5 years ago
• danielzuegner / netgan
  Implementation of the paper "NetGAN: Generating Graphs via Random Walks".
  ☆196Updated 4 years ago
• locuslab / sdp_clustering
  ☆13Updated 4 years ago
• google / gcnn-survey-paper
  ☆173Updated 5 years ago
• dalab / matrix-manifolds
  Source code for the "Computationally Tractable Riemannian Manifolds for Graph Embeddings" paper
  ☆37Updated 5 years ago
• 000Justin000 / gnn-residual-correlation
  ☆20Updated 4 years ago