ICLR2023图神经网络相关的文章整理

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