ICLR'23截稿, 图神经网络依然火热 (附42 篇好文整理)
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转载自 | 图神经网络与推荐系统
作者 | 北冥有鱼
- 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
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版权声明:以上内容为用户推荐收藏至CareerEngine平台,其内容(含文字、图片、视频、音频等)及知识版权均属用户或用户转发自的第三方网站,如涉嫌侵权,请通知[email protected]进行信息删除。如需查看信息来源,请点击“查看原文”。如需洽谈其它事宜,请联系[email protected]。