Simple reference implementation of GraphSAGE.
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Updated
Mar 23, 2018 - Python
Simple reference implementation of GraphSAGE.
This is the PyTorch-0.4.0 implementation of few-shot learning on CIFAR-100 with graph neural networks (GNN)
A simple Pytorch implementation of Gated Graph Neural Networks
graph network and knowledge graph models
Keras implementation of the graph attention networks (GAT) by Veličković et al. (2017; https://arxiv.org/abs/1710.10903)
A convenient wrapper to develop graph neural networks with Keras. Currently under development with the objective of integrating Networkx, Owlready2 and oneM2M for cognitive IoT.
state-lstm in pytorch
Reproduction work of "Neural Relational Inference for Interacting Systems" in Chainer
Open source machine learning for graph-structured data
Re-implementation and extension of the work described in "Learning to Represent Programs with Graphs"
Pytorch Implementation of GNN Meta Attack paper.
learning GNNs
Graph-based Deep Q Network for Web Navigation
library for state-of-the-art graph neural networks
Video Object Segmentation using Graph Neural Networks
Recurrent relational networks (arXiv:1711.08028) implemented in PyTorch
Link prediction algorithm for recommending movies on Netflix Prize data
Fisher-Bures Adversary Graph Convolutional Networks
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