TOP 100值得读的图神经网络----自监督学习与预训练_graphsage应用于图级预测

清华大学的Top 100 GNN papers，其中分了十个方向，每个方向10篇。此篇为自监督学习与预训练方向的阅读笔记。
Top100值得一读的图神经网络| 大家好，我是蘑菇先生，今天带来Top100 GNN Papers盘点文。此外，公众号技术交流群开张啦！可https://mp.weixin.qq.com/s?__biz=MzIyNDY5NjEzNQ==&mid=2247491631&idx=1&sn=dfa36e829a84494c99bb2d4f755717d6&chksm=e809a207df7e2b1117578afc86569fa29ee62eb883fd35428888c0cc0be750faa5ef091f9092&mpshare=1&scene=23&srcid=1026NUThrKm2Vioj874F3gqS&sharer_sharetime=1635227630762&sharer_shareid=80f244b289da8c80b67c915b10efd0a8#rd

架构篇连接：TOP 100值得读的图神经网络----架构_tagagi的博客-CSDN博客https://blog.csdn.net/tagagi/article/details/121318308

同时推荐一个很好的博客： 图对比学习的最新进展_模型GCC以及我们提出的GRACE和同期GraphCL的工作采用了local-local的思路，即直接利用两个经过增强的view中节点的embedding特征，巧妙地绕开了设计一个单射读出函数的需求。 …https://www.sohu.com/a/496046127_121124371论文列表

1. Strategies for pre-training graph neural networks. Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, Leskovec Jure. ICLR 2020.
2. Deep graph infomax. Velikovi Petar, Fedus William, Hamilton William L, Li Pietro, Bengio Yoshua, Hjelm R Devon. ICLR 2019.
3. Inductive representation learning on large graphs. Hamilton Will, Zhitao Ying, Leskovec Jure. NeurIPS 2017.
4. Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. Sun Fan-Yun, Hoffmann Jordan, Verma Vikas, Tang Jian. ICLR 2020.
5. GCC: Graph contrastive coding for graph neural network pre-training. Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, Jie Tang. KDD 2020.
6. Contrastive multi-view representation learning on graphs. Hassani Kaveh, Khasahmadi Amir Hosein. ICML 2020.
7. Graph contrastive learning with augmentations. Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen. NeurIPS 2020.
8. GPT-GNN: Generative pre-training of graph neural networks. Ziniu Hu, Yuxiao Dong, Kuansan Wang, Kai-Wei Chang, Yizhou Sun. KDD 2020.
9. When does self-supervision help graph convolutional networks?. Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen. ICML 2020.
10. Deep graph contrastive representation learning. Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang. GRL+@ICML 2020.

目录

一、图神经网络预训练的策略  STRATEGIES FORPRE-TRAININGGRAPHNEURAL NETWORKS

二、DGI DEEP GRAPH INFOMAX

三、GraphSAGE大型图的归纳表示学习 Inductive Representation Learning on Large Graphs

四、INFOGRAPH: UNSUPERVISED ANDSEMI-SUPERVISED GRAPH-LEVEL REPRESENTATION LEARNING  VIA MUTUAL INFORMATION MAXIMIZATION

五、GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training

六、多视图对比学习 Contrastive multi-view representation learning on graphs

七、GraphCL Graph Contrastive Learning with Augmentations

八、GPT-GNN: Generative Pre-Training of Graph Neural Networks

九、When Does Self-Supervision Help Graph Convolutional Networks?

十、GRACE Deep Graph Contrastive Representation Learning

一、图神经网络预训练的策略  STRATEGIES FORPRE-TRAININGGRAPHNEURAL NETWORKS