| Title | Link | Year |
|---|---|---|
| A new model for learning in graph domains | https://ieeexplore.ieee.org/document/1555942 | 2005 |
| The Graph Neural Network Model | https://ieeexplore.ieee.org/document/4700287 | 2009 |
| Title | Link | Year | Note |
|---|---|---|---|
| Semi-Supervised Classification with Graph Convolutional Networks | https://arxiv.org/abs/1609.02907 | 2016 | GCN |
| Gated Graph Sequence Neural Networks | https://arxiv.org/abs/1511.05493 | 2016 | GGNN |
| Inductive Representation Learning on Large Graphs | https://arxiv.org/abs/1706.02216 | 2017 | GraphSAGE |
| Graph Attention Networks | https://arxiv.org/abs/1710.10903 | 2018 | GAT |
| How Powerful are Graph Neural Networks? | https://arxiv.org/abs/1810.00826 | 2018 | GIN |
| Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks | https://arxiv.org/abs/1810.02244 | 2018 | |
| Representation Learning on Graphs with Jumping Knowledge Networks | https://arxiv.org/abs/1806.03536 | 2018 | |
| Graph Transformer Networks | https://arxiv.org/abs/1911.06455 | 2019 |
| Title | Link | Year |
|---|---|---|
| Relational inductive biases, deep learning, and graph networks | https://arxiv.org/abs/1806.01261 | 2018 |
| Revisiting Graph Neural Networks: All We Have is Low-Pass Filters | https://arxiv.org/abs/1905.09550 | 2019 |
| Graph Neural Networks Exponentially Lose Expressive Power for Node Classification | https://arxiv.org/abs/1905.10947 | 2020 |
| Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View | https://arxiv.org/abs/1909.03211 | 2020 |
| On the Bottleneck of Graph Neural Networks and its Practical Implications | https://arxiv.org/abs/2006.05205 | 2021 |
| Understanding over-squashing and bottlenecks on graphs via curvature | https://arxiv.org/abs/2111.14522 | 2022 |
| On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology | https://arxiv.org/abs/2302.02941 | 2023 |
| WL meet VC | https://arxiv.org/abs/2301.11039 | 2023 |
| ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond | https://arxiv.org/abs/2303.06562 | 2023 |
| Title | Link | Year |
|---|---|---|
| Diffusion Improves Graph Learning | https://arxiv.org/abs/1911.05485 | 2019 |
| Understanding over-squashing and bottlenecks on graphs via curvature | https://arxiv.org/abs/2111.14522 | 2022 |
| DiffWire: Inductive Graph Rewiring via the Lovász Bound | https://arxiv.org/abs/2206.07369 | 2022 |
| DRew: Dynamically Rewired Message Passing with Delay | https://arxiv.org/abs/2305.08018 | 2023 |
| Title | Link | Year |
|---|---|---|
| A Comprehensive Survey on Graph Neural Networks | https://arxiv.org/abs/1901.00596 | 2019 |
| Understanding Pooling in Graph Neural Networks | https://arxiv.org/abs/2110.05292 | 2022 |
| Attending to Graph Transformers | https://arxiv.org/abs/2302.04181 | 2023 |