Graph Convolutional Networks

Learning Convolutional Neural Networks for Graphs

• intro: ICML 2016
• arxiv: http://arxiv.org/abs/1605.05273

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

• arxiv: https://arxiv.org/abs/1606.09375
• github: https://github.com/mdeff/cnn_graph
• github: https://github.com/pfnet-research/chainer-graph-cnn

Semi-Supervised Classification with Graph Convolutional Networks

• arxiv: http://arxiv.org/abs/1609.02907
• github: https://github.com/tkipf/gcn
• blog: http://tkipf.github.io/graph-convolutional-networks/

Graph Based Convolutional Neural Network

• intro: BMVC 2016
• arxiv: http://arxiv.org/abs/1609.08965

How powerful are Graph Convolutions? (review of Kipf & Welling, 2016)

http://www.inference.vc/how-powerful-are-graph-convolutions-review-of-kipf-welling-2016-2/

Graph Convolutional Networks

• blog: http://tkipf.github.io/graph-convolutional-networks/

DeepGraph: Graph Structure Predicts Network Growth

• arxiv: https://arxiv.org/abs/1610.06251

Deep Learning with Sets and Point Clouds

• intro: CMU
• arxiv: https://arxiv.org/abs/1611.04500

Deep Learning on Graphs

• lecture: https://figshare.com/articles/Deep_Learning_on_Graphs/4491686

Robust Spatial Filtering with Graph Convolutional Neural Networks

https://arxiv.org/abs/1703.00792

Modeling Relational Data with Graph Convolutional Networks

https://arxiv.org/abs/1703.06103

Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks

• intro: Imperial College London
• arxiv: https://arxiv.org/abs/1703.02161

Deep Learning on Graphs with Graph Convolutional Networks

• slides: http://tkipf.github.io/misc/GCNSlides.pdf

Deep Learning on Graphs with Keras

• intro:; Keras implementation of Graph Convolutional Networks
• github: https://github.com/tkipf/keras-gcn

Learning Graph While Training: An Evolving Graph Convolutional Neural Network

https://arxiv.org/abs/1708.04675

Graph Attention Networks

• intro: ICLR 2018
• intro: University of Cambridge & Centre de Visio per Computador, UAB & Montreal Institute for Learning Algorithms
• project page: http://petar-v.com/GAT/
• arxiv: https://arxiv.org/abs/1710.10903
• github: https://github.com/PetarV-/GAT

Residual Gated Graph ConvNets

https://arxiv.org/abs/1711.07553

Probabilistic and Regularized Graph Convolutional Networks

• intro: CMU
• arxiv: https://arxiv.org/abs/1803.04489

Videos as Space-Time Region Graphs

https://arxiv.org/abs/1806.01810

Relational inductive biases, deep learning, and graph networks

• intro: DeepMind & Google Brain & MIT & University of Edinburgh
• arxiv: https://arxiv.org/abs/1806.01261

Can GCNs Go as Deep as CNNs?

• project: https://sites.google.com/view/deep-gcns
• arxiv: https://arxiv.org/abs/1904.03751
• slides: https://docs.google.com/presentation/d/1L82wWymMnHyYJk3xUKvteEWD5fX0jVRbCbI65Cxxku0/edit#slide=id.p
• github(official, TensorFlow): https://github.com/lightaime/deep_gcns

GMNN: Graph Markov Neural Networks

• intro: ICML 2019
• ariv: https://arxiv.org/abs/1905.06214
• github: https://github.com/DeepGraphLearning/GMNN

DeepGCNs: Making GCNs Go as Deep as CNNs

• intro: ICCV 2019 Oral
• arxiv: https://arxiv.org/abs/1910.06849
• github: https://github.com/lightaime/deep_gcns_torch
• github: https://github.com/lightaime/deep_gcns

Rethinking pooling in graph neural networks

• intro: NeurIPS 2020
• arxiv: https://arxiv.org/abs/2010.11418