Graphbased Neural Networks
This page is to summarize important materials about graphbased neural networks and relational networks. If I miss some recent works or anyone wants to recommend other references, please let me know.
Background
(You can find many materials for deep neural networks in other places. Here, I mainly cover materials about graphs.)
 Basic Graph Theory by Xavier Bresson, See Lecture 3 and 16
 Spectral Graph Theory by Fan Chung
 Graph Signal Processing GSP by Ortega et al.
 This paper provide an overview of core ideas in GSP and their connection to conventional digital signal processing.
 Signal processing is required to understand the convolution in the spectral domain.
 Keywords : graph theory, spectral graph theory, discrete Fourier transform (DFT)
List of Related Works

Early works using graph structure
 A new model for learning in graph domains
 M. Gori, G. Monfardini, F. Scarselli, IJCNN 2005
 First attempts to generalize neural networks to graphs
 The graph neural network model
 F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini, IEEE Trans. Neural Networks 2009
 These works optimized over the parameterized steady state of some diffusion process (or random walk) on the graph.
 A new model for learning in graph domains

Review paper (highly recommend)
 Geometric deep learning: going beyond Euclidean data
 Michael M. Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, Pierre Vandergheynst, IEEE Signal Processing Magazine 2017
 First review paper of geometric deep learning
 Geometric deep learning: going beyond Euclidean data

Graph Convolutional Networks (GCNs)
 Spectral Networks and Locally Connected Networks on Graphs
 Joan Bruna, Wojciech Zaremba, Arthur Szlam, Yann LeCun, ICLR 2014
 First formulation of CNNs on graphs in the spectral domain
 Deep Convolutional Networks on GraphStructured Data
 Mikael Henaff, Joan Bruna, Yann LeCun, 2015
 Spatial localization of smooth filters in the frequency domain
 Convolutional Networks on Graphs for Learning Molecular Fingerprints
 David Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alan AspuruGuzik, Ryan P. Adams, NIPS 2015
 Gated Graph Sequence Neural Networks
 Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard Zemel, ICLR 2016
 Sliding a filter on the vertices as conventional CNNs, not spectral filtering
 Learning Convolutional Neural Networks for Graphs
 Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov, ICML 2016
 Generalizing the Convolution Operator to extend CNNs to Irregular Domains
 JeanCharles Vialatte, Vincent Gripon, Grégoire Mercier, arXiv 2016
 Generalize CNNs to irregular domains using weight sharing and graphbased operators
 Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering, [PyTorch Code] [TF Code]
 Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst, NIPS 2016
 Spectral CNN with Chebychev polynomial filters (ChebNet)
 Learning Shape Correspondence with Anisotropic Convolutional Neural Networks
 Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Michael M. Bronstein, NIPS 2016
 Anisotropic CNN framework
 SemiSupervised Classification with Graph Convolutional Networks, [Code], [Blog]
 Thomas N. Kipf, Max Welling, ICLR 2017
 Graph Convolutional Networks (GCN) framework, a simplification of ChebNet
 Geometric Deep Learning on Graphs and Manifolds using Mixture Model CNNs
 Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Jan Svoboda, Michael M. Bronstein, CVPR 2017
 MoNets
 Geometric Matrix Completion with Recurrent MultiGraph Neural Networks, [Code]
 Federico Monti, Michael M. Bronstein, Xavier Bresson, NIPS 2017
 Recommendation systems
 CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters
 Ron Levie, Federico Monti, Xavier Bresson, Michael M. Bronstein, arXiv 2017
 Spectral CNN with complex rational filters (CayleyNet)
 Residual Gated Graph ConvNets
 Xavier Bresson, Thomas Laurent, arXiv 2017
 Spectral Networks and Locally Connected Networks on Graphs

Relational Networks (RNs), Relational Reasoning, Interactions
 Interaction networks for learning about objects, relations and physics
 Peter W. Battaglia, Razvan Pascanu, Matthew Lai, Danilo Rezende, Koray Kavukcuoglu, NIPS 2016
 A simple neural network module for relational reasoning, [Deepmind Article], [Code]
 Adam Santoro, David Raposo, David G.T. Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, Timothy Lillicrap, arXiv 2017
 Consider all possible pairs
 Neural Message Passing for Quantum Chemistry
 Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl, ICML 2017
 Pointnet: Deep learning on point sets for 3d classification and segmentation
 Charles R. Qi, Hao Su, Kaichun Mo, Leonidas J. Guibas, CVPR2017
 SchNet: A continuousfilter convolutional neural network for modeling quantum interactions
 Kristof T. Schütt, PieterJan Kindermans, Huziel E. Sauceda, Stefan Chmiela, Alexandre Tkatchenko, KlausRobert Müller, NIPS 2017
 VAIN: Attentional Multiagent Predictive Modeling
 Yedid Hoshen, NIPS 2017
 Modeling Relational Data with Graph Convolutional Networks
 Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling, arXiv 2017
 Graph Attention Networks, [Code]
 Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio, ICLR 2018
 Interaction networks for learning about objects, relations and physics

Graph AutoEncoder (GAE)
 Variational Graph AutoEncoders, [Code]
 Thomas N. Kipf, Max Welling, NIPS Workshop on Bayesian Deep Learning 2016
 Question: Why the adjacency matrix is reconstructed rather than the feature matrix?
 Modeling Relational Data with Graph Convolutional Networks
 Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling, 2017
 Graph Convolutional Matrix Completion
 Rianne van den Berg, Thomas N. Kipf, Max Welling, 2017
 Variational Graph AutoEncoders, [Code]

Other Applications using Graphbased Neural Networks
 Diffusion Convolutional Recurrent Neural Network: DataDriven Traffic Forecasting , [Code]
 Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu, ICLR 2018
 Automatically Inferring Data Quality for Spatiotemporal Forecasting
 Sungyong Seo, Arash Mohegh, George BanWeiss, Yan Liu, ICLR 2018
 Diffusion Convolutional Recurrent Neural Network: DataDriven Traffic Forecasting , [Code]
Tutorials or Workshops
 IPAM18 Workshop, New Deep Learning Techniques
 NIPS17 Tutorial, Geometric Deep Learning on Graphs and Manifolds
 CVPR17 Tutorial, Geometric Deep Learning on Graphs
Useful Resources
 Kipf's blog
 Geometric Deep Learning highly recommended
 CVPR17 tutorial, Geometric and Semantic 3D Reconstruction, 240MB
 How do I generalize convolution of neural networks to graphs?, Defferrard's answers in Quora
 PointNet
List of Researchers
 Thomas Kipf, University of Amsterdam
 Joan Bruna, NYU
 Michaël Defferrard, EPFL
 Xavier Bresson, NTU
 Federico Monti, Università della Svizzera Italiana
 Michael M. Bronstein, Università della Svizzera Italiana