ICLR'23截稿, 图神经网络依然火热 (附42 篇好文整理)

收藏一下ICLR 2023图神经网络相关的文章，存下来慢慢看，后面会慢慢更新一些文章总结

1. Graph Attention Retrospective Kimon Fountoulakis (Waterloo)
2. Limitless Stability for Graph Convolutional Networks
3. The Graph Learning Attention Mechanism: Learnable Sparsification Without Heuristics
4. Network Controllability Perspectives on Graph Representation
5. Graph Contrastive Learning Under Heterophily: Utilizing Graph Filters to Generate Graph Views
6. Spectral Augmentation for Self-Supervised Learning on Graphs
7. Simple and Deep Graph Attention Networks
8. Agent-based Graph Neural Networks Karolis Martinkus (ETH), Pál András Papp (ETH), Benedikt Schesch (ETH) Roger Wattenhofer (ETH)
9. A Class-Aware Representation Refinement Framework for Graph Classification Jiaxing Xu, Jinjie Ni, Sophi Shilpa Gururajapathy & Yiping Ke (NTU)
10. ReD-GCN: Revisit the Depth of Graph Convolutional Network
11. Revisiting Graph Adversarial Attack and Defense From a Data Distribution Perspective
12. Simple Spectral Graph Convolution from an Optimization Perspective
13. GraphEditor: An Efficient Graph Representation Learning and Unlearning Approach Weilin Cong, Mehrdad Mahdavi (PSU)
14. Specformer: Spectral Graph Neural Networks Meet Transformers
15. DiGress: Discrete Denoising diffusion for graph generation Clement Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, Pascal Frossard (EPFL)
16. ASGNN: Graph Neural Networks with Adaptive Structure
17. DeepGRAND: Deep Graph Neural Diffusion
18. Empowering Graph Representation Learning with Test-Time Graph Transformation
19. The Impact of Neighborhood Distribution in Graph Convolutional Networks
20. NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs
21. Wide Graph Neural Network
22. How Powerful is Implicit Denoising in Graph Neural Networks Songtao Liu (PSU), Rex Ying (Yale), Hanze Dong (HKUST), Lu Lin (PSU), Jinghui Chen (PSU), Dinghao Wu (PSU)
23. Learnable Graph Convolutional Attention Networks
24. Revisiting Robustness in Graph Machine Learning
25. Graph Neural Bandits Parnian Kassraie (ETH), Andreas Krause (ETH), Ilija Bogunovic (UCL)
26. Learning Graph Neural Network Topologies
27. Affinity-Aware Graph Networks Ameya Velingker (Google Research), Ali Kemal Sinop (Google Research), Ira Ktena (DeepMind), Petar Velickovic (DeepMind), Sreenivas Gollapudi (Google Research)
28. Diffusing Graph Attention
29. Relational Curriculum Learning for Graph Neural Networks
30. Stable, Efficient, and Flexible Monotone Operator Implicit Graph Neural Networks
31. Distributional Signals for Node Classification in Graph Neural Networks
32. Rewiring with Positional Encodings for GNNs Rickard Bruel-Gabrielsson (MIT), Mikhail Yurochkin (MIT-IBM) Justin Solomon (MIT)
33. Learning MLPs on Graphs: A Unified View of Effectiveness, Robustness, and Efficiency
34. Fair Graph Message Passing with Transparency Zhimeng Jiang (TAMU), Xiaotian Han (TAMU), Chao Fan (TAMU), Zirui Liu (Rice), Na Zou (TAMU), Ali Mostafavi (TAMU), Xia Hu (Rice)
35. Sign and Basis Invariant Networks for Spectral Graph Representation Learning Derek Lim (MIT), Joshua Robinson (MIT), Lingxiao Zhao (CMU), Tess Smidt (MIT), Suvrit Sra (MIT) Haggai Maron (NVIDIA Research) Stefanie Jegelka (MIT)
36. Graph Neural Networks Are More Powerful Than We Think Charilaos I. Kanatsoulis, Alejandro Ribeiro (UPenn)
37. Robust Graph Representation Learning via Predictive Coding
38. Universal Graph Neural Networks without Message Passing
39. Fair Attribute Completion on Graph with Missing Attributes
40. Asynchronous Message Passing: A New Framework for Learning in GraphsLukas Faber, Roger Wattenhofer (ETH)
41. Graph Neural Networks as Gradient Flows: understanding graph convolutions via energyFrancesco Di Giovanni, James Rowbottom, Benjamin P. Chamberlain, Thomas Markovich, Michael M. Bronstein (Twitter)
42. Rethinking the Expressive Power of GNNs via Graph Biconnectivity