Graph Neural Network Models in Pytorch
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Updated
Mar 28, 2019
Graph Neural Network Models in Pytorch
PyG (a geometric extension library for PyTorch) implementation of several Graph Neural Networks (GNNs): GCN, GAT, GraphSAGE, etc.
Graph Network in Tensorflow
A pytorch implemention of GCN-GAN for temporal link prediction.
A Pure Keras Implementation of Knowledge Graph Convolution Network for Recommendation
Deep Vessel Segmentation by Learning Graphical Connectivity
PyTorch implementation of "DeepSphere: a Graph-based Spherical CNN", Defferard et al., 2019.
Code to reproduce the results in the paper Supervised Learning on Relational Databases with Graph Neural Networks.
Non-local Attention Learning on Large Heterogeneous Information Networks (IEEE BigData 2019)
Representation-Learning-on-Heterogeneous-Graph
Learning record about Stanford cs224w: Machine Learning with Graphs 2019
Discovering Strategic Behaviors for Collaborative Content-Production in Social Networks (WWW 2020)
Aggregate information of neighbor nodes to produce a new feature representation for each node. Tested in image retrieval task.
Trying to apply Deep RL + Geometric DL to graphs exploration
Graph Convolutional Networks, Graph Attention Networks, Gated Graph Neural Net, Mixhop
Heterogeneous Graph Neural Network
Neural Graph Collaborative Filtering, SIGIR2019
This implementation of GAT is revised to predict antiviral activity against SARS-CoV-2 (COVID-19).
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