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转载自 | 图神经网络与推荐系统
作者 | 北冥有鱼
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
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