资源分享 | 深度图表示学习的论文/视频教程/博客合集

推荐一个清华叉院好友总结的深度图表示学习GitHub仓库, 收集了最近几年AI会议中, 他读过的比较有意思的关于深度图表示学习的论文, 视频教程, 和相关博客.

image-20210728113415381

链接: https://github.com/zlpure/awesome-graph-representation-learning

主要内容

image-20210728143937554

论文

这部分先集中整理了深度图表示学习的相关综述及必读论文, 然后按照会议和时间分类整理19年-21年的一些最新进展.

ICML 2021

• Training Graph Neural Networks with 1000 Layers Guohao Li, Matthias Müller, Bernard Ghanem, Vladlen Koltun Deeper GNNs
• GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training Tianle Cai, Shengjie Luo, Keyulu Xu, Di He, Tie-Yan Liu, Liwei Wang Training mechanism

WWW 2021

• Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework Cheng Yang, Jiawei Liu, Chuan Shi Graph + knowledge distillation
• Graph Contrastive Learning with Adaptive Augmentation Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang Graph contrastive learning

更多论文见作者的GitHub.

视频教程

• Graph Representation Learning for Drug Discovery Jian Tang 2022
• Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs Jiong Zhu 2021
• Theoretical Foundations of Graph Neural Networks Petar Veličković 2021

博客

• Graph Neural Networks as Neural Diffusion PDEs Michael Bronstein 2021
• Graph Contrastive learning Yanqiao Zhu 2021
• Temporal Graph Networks Michael Bronstein 2020