42篇ICLR2023圖神經(jīng)網(wǎng)絡(luò)論文
來(lái)源：轉(zhuǎn)載自圖神經(jīng)網(wǎng)絡(luò)與推薦系統(tǒng)
作者：北冥有魚?編輯：學(xué)姐
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Graph Attention Retrospective??Kimon Fountoulakis (Waterloo)
Limitless Stability for Graph Convolutional Networks
The Graph Learning Attention Mechanism: Learnable Sparsification Without Heuristics
Network Controllability Perspectives on Graph Representation
Graph Contrastive Learning Under Heterophily: Utilizing Graph Filters to Generate Graph Views
Spectral Augmentation for Self-Supervised Learning on Graphs
Simple and Deep Graph Attention Networks
Agent-based Graph Neural Networks?Karolis Martinkus (ETH), Pál András Papp (ETH), Benedikt Schesch (ETH) Roger Wattenhofer (ETH)
A Class-Aware Representation Refinement Framework for Graph Classification?Jiaxing Xu, Jinjie Ni, Sophi Shilpa Gururajapathy & Yiping Ke (NTU)
ReD-GCN: Revisit the Depth of Graph Convolutional Network
Revisiting Graph Adversarial Attack and Defense From a Data Distribution Perspective
Simple Spectral Graph Convolution from an Optimization Perspective
GraphEditor: An Efficient Graph Representation Learning and Unlearning Approach?Weilin Cong, Mehrdad Mahdavi (PSU)
Specformer: Spectral Graph Neural Networks Meet Transformers
DiGress: Discrete Denoising diffusion for graph generation Clement Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, Pascal Frossard (EPFL)
ASGNN: Graph Neural Networks with Adaptive Structure
DeepGRAND: Deep Graph Neural Diffusion
Empowering Graph Representation Learning with Test-Time Graph Transformation
The Impact of Neighborhood Distribution in Graph Convolutional Networks
NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs
Wide Graph Neural Network
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)
Learnable Graph Convolutional Attention Networks
Revisiting Robustness in Graph Machine Learning
Graph Neural Bandits?Parnian Kassraie (ETH), Andreas Krause (ETH), Ilija Bogunovic (UCL)
Learning Graph Neural Network Topologies
Affinity-Aware Graph Networks?Ameya Velingker (Google Research), Ali Kemal Sinop (Google Research), Ira Ktena (DeepMind), Petar Velickovic (DeepMind), Sreenivas Gollapudi (Google Research)
Diffusing Graph Attention
Relational Curriculum Learning for Graph Neural Networks
Stable, Efficient, and Flexible Monotone Operator Implicit Graph Neural Networks
Distributional Signals for Node Classification in Graph Neural Networks
Rewiring with Positional Encodings for GNNs?Rickard Bruel-Gabrielsson (MIT), Mikhail Yurochkin (MIT-IBM) Justin Solomon (MIT)
Learning MLPs on Graphs: A Unified View of Effectiveness, Robustness, and Efficiency
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)
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)
Graph Neural Networks Are More Powerful Than We Think?Charilaos I. Kanatsoulis, Alejandro Ribeiro (UPenn)
Robust Graph Representation Learning via Predictive Coding
Universal Graph Neural Networks without Message Passing
Fair Attribute Completion on Graph with Missing Attributes
Asynchronous Message Passing: A New Framework for Learning in GraphsLukas Faber, Roger Wattenhofer (ETH)
Graph Neural Networks as Gradient Flows: understanding graph convolutions via energyFrancesco Di Giovanni, James Rowbottom, Benjamin P. Chamberlain, Thomas Markovich, Michael M. Bronstein (Twitter)
Rethinking the Expressive Power of GNNs via Graph Biconnectivity
