5.1節 はじめに
| タイトル | 著者名 | 年 | リンク | 登場順 |
|---|---|---|---|---|
| Graph neural networks for ranking Web pages | Scarselli et al | 2005 | リンク | 1 |
| The Graph Neural Network Model | Scarselli et al | 2008 | リンク | 2 |
5.3節 グラフフィルタ
| タイトル | 著者名 | 年 | リンク | 登場順 |
|---|---|---|---|---|
| The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains | Shuman et al | 2013 | arxiv | 1 |
| Spectral Networks and Locally Connected Networks on Graphs | Bruna et al | 2013 | arxiv | 2 |
| Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering | Defferrard et al | 2016 | arxiv | 3 |
| Diffusion-Convolutional Neural Networks | Atwood and Towsley | 2016 | arxiv | 4 |
| Semi-Supervised Classification with Graph Convolutional Networks | Kipf and Welling | 2016a | arxiv | 5 |
| Graph neural networks for ranking Web pages | Scarselli et al | 2005 | リンク | 6 |
| The Graph Neural Network Model | Scarselli et al | 2008 | リンク | 7 |
| Inductive Representation Learning on Large Graphs | Hamilton et al | 2017a | arxiv | 8 |
| Graph Attention Networks | Veličković et al | 2017 | arxiv | 9 |
| Attention Is All You Need | Vaswani et al | 2017 | arxiv | 10 |
| Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs | Simonovsky and Komodakis | 2017 | arxiv | 11 |
| Gated Graph Sequence Neural Networks | Li et al | 2015 | arxiv | 12 |
| Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks | Monti et al | 2017 | arxiv | 13 |
| Neural Message Passing for Quantum Chemistry | Gilmer et al | 2017 | arxiv | 14 |
5.4節 グラフプーリング
| タイトル | 著者名 | 年 | リンク | 登場順 |
|---|---|---|---|---|
| Gated Graph Sequence Neural Networks | Li et al | 2015 | arxiv | 1 |
| Graph U-Nets | Gao and Ji | 2019 | arxiv | 2 |
| Self-Attention Graph Pooling | Lee et al | 2019 | arxiv | 3 |
| Hierarchical Graph Representation Learning with Differentiable Pooling | Ying et al | 2018c | arxiv | 4 |
| Graph Convolutional Networks with EigenPooling | Ma et al | 2019b | arxiv | 5 |
5.7節 参考文献
| タイトル | 著者名 | 年 | リンク | 登場順 |
|---|---|---|---|---|
| Deep Graph Kernels | Yanardag and Vishwanathan | 2015 | リンク | 1 |
| Learning Convolutional Neural Networks for Graphs | Niepert et al | 2016 | arxiv | 2 |
| Graph Classification using Structural Attention | Lee et al | 2018 | リンク | 3 |
| Adaptive Graph Convolutional Neural Networks | Li et al | 2018c | arxiv | 4 |
| Large-Scale Learnable Graph Convolutional Networks | Gao et al | 2018a | arxiv | 5 |
| GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs | Zhang et al | 2018a | arxiv | 6 |
| GeniePath: Graph Neural Networks with Adaptive Receptive Paths | Liu et al | 2019b | arxiv | 7 |
| Deep Graph Infomax | Veličković et al | 2019 | arxiv | 8 |
| Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks | Morris et al | 2019 | arxiv | 9 |
| Kronecker Attention Networks | Gao et al | 2020 | arxiv | 10 |
| StructPool: Structured Graph Pooling via Conditional Random Fields | Yuan and Ji | 2019a | リンク | 11 |
| Graph Neural Networks: A Review of Methods and Applications | Zhou et al | 2018a | arxiv | 12 |
| A Comprehensive Survey on Graph Neural Networks | Wu et al | 2020 | arxiv | 13 |
| Graph Convolution over Pruned Dependency Trees Improves Relation Extraction | Zhang et al | 2018c | arxiv | 14 |
| Fast Graph Representation Learning with PyTorch Geometric | Fey and Lenssen | 2019 | arxiv | 15 |
| Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks | Wang et al | 2019e | arxiv | 16 |