Alternatives and similar repositories for MixHop-and-N-GCN

Users that are interested in MixHop-and-N-GCN are comparing it to the libraries listed below

• benedekrozemberczki / SGCN
  A PyTorch implementation of "Signed Graph Convolutional Network" (ICDM 2018).
  ☆274Updated 2 years ago
• benedekrozemberczki / GAM
  A PyTorch implementation of "Graph Classification Using Structural Attention" (KDD 2018).
  ☆273Updated 2 years ago
• benedekrozemberczki / APPNP
  A PyTorch implementation of "Predict then Propagate: Graph Neural Networks meet Personalized PageRank" (ICLR 2019).
  ☆368Updated 2 years ago
• benedekrozemberczki / AttentionWalk
  A PyTorch Implementation of "Watch Your Step: Learning Node Embeddings via Graph Attention" (NeurIPS 2018).
  ☆326Updated 2 years ago
• benedekrozemberczki / SEAL-CI
  A PyTorch implementation of "Semi-Supervised Graph Classification: A Hierarchical Graph Perspective" (WWW 2019)
  ☆210Updated 2 years ago
• JiaxuanYou / P-GNN
  Position-aware Graph Neural Networks
  ☆399Updated 4 years ago
• bknyaz / graph_attention_pool
  Attention over nodes in Graph Neural Networks using PyTorch (NeurIPS 2019)
  ☆285Updated 4 years ago
• DeepGraphLearning / GMNN
  Graph Markov Neural Networks
  ☆410Updated 5 years ago
• gasteigerjo / ppnp
  PPNP & APPNP models from "Predict then Propagate: Graph Neural Networks meet Personalized PageRank" (ICLR 2019)
  ☆322Updated 7 months ago
• matenure / FastGCN
  The sample codes for our ICLR18 paper "FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling""
  ☆526Updated 4 years ago
• Tiiiger / SGC
  official implementation for the paper "Simplifying Graph Convolutional Networks"
  ☆842Updated 3 years ago
• dbusbridge / gcn_tutorial
  A tutorial on Graph Convolutional Neural Networks
  ☆318Updated 4 years ago
• bkj / pytorch-graphsage
  Representation learning on large graphs using stochastic graph convolutions.
  ☆138Updated 7 years ago
• vuptran / graph-representation-learning
  [DSAA 2018] Autoencoders for Link Prediction and Semi-Supervised Node Classification
  ☆256Updated 5 years ago
• snap-stanford / GraphRNN
  ☆423Updated 6 years ago
• fmonti / mgcnn
  Multi-Graph Convolutional Neural Networks
  ☆255Updated 7 years ago
• lrjconan / LanczosNetwork
  Lanczos Network, Graph Neural Networks, Deep Graph Convolutional Networks, Deep Learning on Graph Structured Data, QM8 Quantum Chemistry …
  ☆314Updated 5 years ago
• benedekrozemberczki / Splitter
  A Pytorch implementation of "Splitter: Learning Node Representations that Capture Multiple Social Contexts" (WWW 2019).
  ☆213Updated 2 years ago
• leowyy / GraphTSNE
  PyTorch Implementation of GraphTSNE, ICLR’19
  ☆135Updated 6 years ago
• bknyaz / graph_nn
  Graph Classification with Graph Convolutional Networks in PyTorch (NeurIPS 2018 Workshop)
  ☆334Updated 4 years ago
• RexYing / diffpool
  ☆492Updated 4 years ago
• shagunsodhani / Graph-Reading-Group
  Weekly reading group on Graphs at Mila
  ☆280Updated 6 years ago
• benedekrozemberczki / GraphWaveMachine
  A scalable implementation of "Learning Structural Node Embeddings Via Diffusion Wavelets (KDD 2018)".
  ☆187Updated 2 years ago
• danielzuegner / netgan
  Implementation of the paper "NetGAN: Generating Graphs via Random Walks".
  ☆195Updated 4 years ago
• lucfra / LDS-GNN
  Learning Discrete Structures for Graph Neural Networks (TensorFlow implementation)
  ☆197Updated last year
• benedekrozemberczki / ClusterGCN
  A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019).
  ☆799Updated 2 years ago
• facebookresearch / hgnn
  Hyperbolic Graph Neural Networks
  ☆236Updated 5 years ago
• deezer / linear_graph_autoencoders
  Source code from the NeurIPS 2019 workshop article "Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks" (G. Salha, R…
  ☆133Updated 4 years ago
• Luckick / EAGCN
  Multi-View Spectral Graph Convolution with Consistent Edge Attention for Molecular Modeling
  ☆203Updated 3 years ago
• samihaija / mixhop
  Official Implementation of ICML 2019 Paper. MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing; an…
  ☆124Updated 6 years ago