Graph-based Neural Networks

This page is to summarize important materials about graph-based 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.
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
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 Graph-Structured 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 Aspuru-Guzik, 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
  - Jean-Charles Vialatte, Vincent Gripon, Grégoire Mercier, arXiv 2016
  - Generalize CNNs to irregular domains using weight sharing and graph-based 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
- Semi-Supervised 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 Multi-Graph 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
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 continuous-filter convolutional neural network for modeling quantum interactions
  - Kristof T. Schütt, Pieter-Jan Kindermans, Huziel E. Sauceda, Stefan Chmiela, Alexandre Tkatchenko, Klaus-Robert Müller, NIPS 2017
- VAIN: Attentional Multi-agent 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
Graph Auto-Encoder (GAE)
- Variational Graph Auto-Encoders, [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
Other Applications using Graph-based Neural Networks
- Diffusion Convolutional Recurrent Neural Network: Data-Driven 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 Ban-Weiss, Yan Liu, ICLR 2018

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