Alternatives and similar repositories for CGMM

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

• 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
• delta2323 / gnn-asymptotics
  Code for Graph Neural Networks Exponentially Lose Expressive Power for Node Classification.
  ☆30Updated 5 years ago
• NNReasoning / What-Can-Neural-Networks-Reason-About
  ☆44Updated 4 years ago
• mitmul / chainer-nri
  Reproduction work of "Neural Relational Inference for Interacting Systems" in Chainer
  ☆34Updated 6 years ago
• IBM / IPC-graph-data
  IPC: A Graph Data Set Compiled from International Planning Competitions
  ☆46Updated 5 years ago
• yuj-umd / graphRNN
  Codes for the paper "Learning Graph-Level Representations with Gated Recurrent Neural Networks"
  ☆29Updated 6 years ago
• matenure / OTCoarsening
  Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport
  ☆37Updated 4 years ago
• Hanjun-Dai / steady_state_embedding
  Learning Steady-States of Iterative Algorithms over Graphs
  ☆40Updated 6 years ago
• KelestZ / CondGen
  Conditional Structure Generation through Graph Variational Generative Adversarial Nets, NeurIPS 2019.
  ☆54Updated 5 years ago
• PurdueMINDS / RelationalPooling
  ☆35Updated 6 years ago
• amirkhas / GraphMemoryNet
  Memory-Based Graph Networks
  ☆103Updated 3 years ago
• xbresson / spatial_graph_convnets
  PyTorch implementation of residual gated graph ConvNets, ICLR’18
  ☆122Updated 6 years ago
• dalab / matrix-manifolds
  Source code for the "Computationally Tractable Riemannian Manifolds for Graph Embeddings" paper
  ☆36Updated 5 years ago
• nkeriven / random-graph-gnn
  ☆12Updated 4 years ago
• Cyanogenoid / perm-optim
  [ICLR 2019] Learning Representations of Sets through Optimized Permutations
  ☆36Updated 6 years ago
• malllabiisc / ConfGCN
  AISTATS 2019: Confidence-based Graph Convolutional Networks for Semi-Supervised Learning
  ☆59Updated 6 years ago
• ML-on-structures / graph-lstm
  LSTM implementation, and multi-layer LSTMs for learning on graph neighborhoods
  ☆80Updated 8 years ago
• wooden-spoon / relational-ERM
  Software relating to relational empirical risk minimization
  ☆17Updated 4 years ago
• ermongroup / graphite
  Code for Graphite iterative graph generation
  ☆59Updated 6 years ago
• giannisnik / cnn-graph-classification
  A convolutional neural network for graph classification in PyTorch
  ☆91Updated 6 years ago
• chentingpc / gfn
  Code for "Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification"
  ☆52Updated 5 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
• melifluos / Hyperbolic-Embedding
  Learning neural network embeddings in hyperbolic spaces
  ☆14Updated 5 years ago
• leowyy / GraphTSNE
  PyTorch Implementation of GraphTSNE, ICLR’19
  ☆134Updated 6 years ago
• Cyanogenoid / fspool
  [ICLR 2020] FSPool: Learning Set Representations with Featurewise Sort Pooling
  ☆41Updated 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…
  ☆132Updated 4 years ago
• delta2323 / GB-GNN
  Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks
  ☆13Updated 5 years ago
• FerranAlet / modular-metalearning
  ☆78Updated 4 years ago
• seuqaj114 / paig
  Code for the paper Physics-as-Inverse-Graphics: Joint Unsupervised Learning of Objects and Physics from Video
  ☆42Updated 2 years ago