Awesome Geometric Deep Learning

Review

1. Geometric deep learning: going beyond Euclidean data (IEEE Signal Processing Magazine 2017)
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2. Graph Neural Networks: A Review of Methods and Applications (2019)
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3. A Comprehensive Survey on Graph Neural Networks (2019)
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Articles

1. The graph neural network model (2009)
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2. Spectral Networks and Locally Connected Networks on Graphs (ICLR 2014)
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3. Deep Convolutional Networks on Graph-Structured Data (arXiv 2015)
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4. Diffusion-Convolutional Neural Networks (2015)
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5. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering (NIPS 2016)
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   • [Python Reference]
6. Semi-Supervised Classification with Graph Convolutional Networks (ICLR 2017)
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7. Convolutional Networks on Graphs for Learning Molecular Fingerprints (NIPS 2015)
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8. Geodesic Convolutional Neural Networks on Riemannian Manifolds (3dRR 2015)
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9. Learning shape correspondence with anisotropic convolutional neural networks (NIPS 2016)
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10. Discriminative embeddings of latent variable models for structured data (ICML 2016)
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11. SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels (CVPR 2018)
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12. Neural Message Passing for Quantum Chemistry (ICML 2017)
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13. Graph Attention Networks (ICLR 2018)
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14. Weisfeiler and Lemon Go Neural: Higher-order Graph Neural Networks (AAAI 2019)
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15. How Powerful are Graph Neural Networks? (ICLR 2019)
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16. Graph Neural Networks with Convolutional ARMA Filters (CoRR 2019)
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17. Simplifying Graph Convolutional Networks (CoRR 2019)
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18. Combining Neural Networks with Personalized PageRank for Classification on Graphs (ICLR 2019)
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19. Attention-based Graph Neural Networks for Semi-Supervised Learning (CoRR 2017)
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20. Modeling Relational Data with Graph Convolutional Networks (ESWC 2018)
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21. Dynamic Graph CNN for Learning on Point Clouds (CoRR 2018)
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22. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation (CVPR 2017)
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23. PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space (NIPS 2017)
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24. PointCNN: Convolution On X-Transformed Points (NeurIPS 2018)
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25. Geometric Deep Learning on Graphs and Manifolds using Mixture Model CNNs (CVPR 2017)
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26. SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation (CVPR 2017)
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27. Relational Inductive Biases, Deep Learning, and Graph Networks (CoRR 2018)
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28. Gated Graph Sequence Neural Networks (ICLR 2016)
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29. Order Matters: Sequence to Sequence for Sets (ICLR 2016)
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30. Structured Sequence Modeling with Graph Convolutional Recurrent Networks (2015)
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31. Deep Learning on Lie Groups for Skeleton-based Action Recognition (CVPR 2017)
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32. Geometric matrix completion with recurrent multi-graph neural networks (NIPS 2017)
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33. Deep Functional Maps: Structured Prediction for Dense Shape Correspondence (2017)
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34. CayleyNets: Graph convolutional neural networks with complex rational spectral filters (2017)
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35. Deriving Neural Architectures from Sequence and Graph Kernels (ICML 2017)
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36. SchNet: A continuous-filter convolutional neural network for modeling quantum interactions (NIPS 2017)
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37. Convolutional Neural Networks on Surfaces via Seamless Toric Covers (2017)
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38. Weighted Graph Cuts without Eigenvectors: A Multilevel Approach (PAMI 2007)
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39. An End-to-End Deep Learning Architecture for Graph Classification (AAAI 2018)
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40. Hierarchical Graph Representation Learning with Differentiable Pooling (NeurIPS 2018)
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41. Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs (CVPR 2017)
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42. Towards Sparse Hierarchical Graph Classifiers (NeurIPS-W 2018)
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43. Deep Graph Infomax (ICLR 2019)
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44. Graph U-Net (ICLR 2019 Submission)
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45. Inductive Representation Learning on Large Graphs (NIPS 2017)
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46. Learning Convolutional Neural Networks for Graphs (ICML 2016)
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