Graphs-related Works

• Graph Convolutional Networks (GCNs)
• Graph Auto-Encoder (GAE)

Prerequisites

• Graph signal processing

List of related works

• Graph Convolutional Networks (GCNs)

  • Spectral Networks and Locally Connected Networks on Graphs, Joan Bruna, Wojciech Zaremba, Arthur Szlam, Yann LeCun, ICLR 2014 (First spectral CNN on graphs)
  • Deep Convolutional Networks on Graph-Structured Data, Mikael Henaff, Joan Bruna, Yann LeCun, 2015 (Spectral CNN with smooth multiplier)
  • Convolutional Networks on Graphs for Learning Molecular Fingerprints, David Duvenaud et al., NIPS 2015
  • Gated Graph Sequence Neural Networks, Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard Zemel, ICLR 2016
  • Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering, 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, Thomas N. Kipf, Max Welling, ICLR 2017, (Graph Convolutional Networks (GCN) framework, a simplification of ChebNet)
  • 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)
  • CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters, Ron Levie, Federico Monti, Xavier Bresson, Michael M. Bronstein, arXiv 2017
  • Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks, Federico Monti, Michael M. Bronstein, Xavier Bresson, NIPS 2017, (Spectral CNN with complex rational filters (CayleyNet))

• Graph Auto-Encoder (GAE)

  • Variational Graph Auto-Encoders, Thomas N. Kipf, Max Welling, NIPS Workshop on Bayesian Deep Learning 2016
  • 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

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

• Graph Auto-Encoder
• 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
• Michael M. Bronstein, Università della Svizzera Italiana