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Graph contrastive learning (GCL) has emerged as a dominant technique for graph representation learning which maximizes the mutual information between paired graph augmentations that share the same semantics.
PAC-Bayesian model averaging
A. David McAllester. 1999 · 1999
Earlier work this paper cites.
Some PAC-Bayesian Theorems
A. David McAllester and Jonathan Baxter. 1999 · 1999
Earlier work this paper cites.
Distinguishing Enzyme Structures from Non-enzymes Without Alignments
D. Paul Dobson and J. Andrew Doig. 2003 · 2003
Earlier work this paper cites.
Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. 2020 · 2006
Earlier work this paper cites.
IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning
Kaspar Riesen and Horst Bunke. 2008 · 2008
Earlier work this paper cites.
Ecient graphlet kernels for large graph comparison
Nino Shervashidze, V. N. S. Vishwanathan, H. Tobias Petri, Kurt Mehlhorn, and M. Karsten Borgwardt. 2009 · 2009
Earlier work this paper cites.
Weisfeiler-Lehman Graph Kernels
Nino Shervashidze, Pascal Schweitzer, Jan van Erik Leeuwen, Kurt Mehlhorn, and M. Karsten Borgwardt. 2011 · 2011
Earlier work this paper cites.
Explaining and Harnessing Adversarial Examples
J. Ian Goodfellow, Jonathon Shlens, and Christian Szegedy. 2015 · 2015
Earlier work this paper cites.
Deep Graph Kernels
Pinar Yanardag and V. N. S. Vishwanathan. 2015 · 2015
Earlier work this paper cites.
Discriminative Embeddings of Latent Variable Models for Structured Data
Hanjun Dai, Bo Dai, and Le Song. 2016 · 2016
Earlier work this paper cites.
node2vec: Scalable Feature Learning for Networks
Aditya Grover and Jure Leskovec. 2016 · 2016
Earlier work this paper cites.
Variational Graph Auto-Encoders
N. Thomas Kipf and Max Welling. 2016a · 2016
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2016b · 2016
Earlier work this paper cites.
Improved Deep Metric Learning with Multi-class N-pair Loss Objective
Kihyuk Sohn. 2016 · 2016
Earlier work this paper cites.
On Sampling Strategies for Neural Network-based Collaborative Filtering
Ting Chen, Yizhou Sun, Yue Shi, and Liangjie Hong. 2017 · 2017
Earlier work this paper cites.
graph2vec: Learning Distributed Representations of Graphs
Annamalai Narayanan, Mahinthan Chandramohan, Rajasekar Venkatesan, Lihui Chen, Yang Liu, and Shantanu Jaiswal. 2017 · 2017
Earlier work this paper cites.
Exploring Generalization in Deep Learning
Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, and Nathan Srebro. 2017 · 2017
Earlier work this paper cites.
Sub2Vec: Feature Learning for Subgraphs
Bijaya Adhikari, Yao Zhang, Naren Ramakrishnan, and Aditya B. Prakash. 2018 · 2018
Earlier work this paper cites.
Adversarial Attack on Graph Structured Data
Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, and Le Song. 2018 · 2018
Earlier work this paper cites.
Unsupervised feature learning via non-parametric instance discrimination
Zhirong Wu, Yuanjun Xiong, X Stella Yu, and Dahua Lin. 2018 · 2018
Cited alongside, same era.
Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification
Ting Chen, Song Bian, and Yizhou Sun. 2019 · 2019
Cited alongside, same era.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Towards Deep Learning Models Resistant to Adversarial Attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2019 · 2019
Cited alongside, same era.
Representation Learning with Contrastive Predictive Coding
van den Aäron Oord, Yazhe Li, and Oriol Vinyals. 2019 · 2019
Cited alongside, same era.
Self-Supervised Graph Transformer on Large-Scale Molecular Data
Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying WEI, Wenbing Huang, and Junzhou Huang. 2020 · 2020
Later among the works it cites.
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization
Fan-Yun Sun, Jordan Hoffman, Vikas Verma, and Jian Tang. 2020 · 2020
Later among the works it cites.
A Simple Framework for Contrastive Learning of Visual Representations
Chen Ting, Kornblith Simon, Norouzi Mohammad, and Hinton Geoffrey. 2020 · 2020
Later among the works it cites.
Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere
Wang Tongzhou and Isola Phillip. 2020 · 2020
Later among the works it cites.
Graph Contrastive Learning with Augmentations. In Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., 5812–5823
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020 · 2020
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Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, and Jian Tang. 2019 · 2019
Cited alongside, same era.
Understanding Adversarial Robustness Through Loss Landscape Geometries
Vinay Prabhu Uday, Dian Yap Ang, Xu Joyce, and Whaley John. 2019 · 2019
Cited alongside, same era.
Deep Graph Infomax
Petar Velickovic, William Fedus, L. William Hamilton, Pietro Liò, Yoshua Bengio, and Devon R. Hjelm. 2019 · 2019
Cited alongside, same era.
How Powerful are Graph Neural Networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019b · 2019
Cited alongside, same era.
An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs
Rozemberczki Benedek, Kiss Oliver, and Sarkar Rik. 2020 · 2020
Cited alongside, same era.
On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them
Liu Chen, Salzmann Mathieu, Lin Tao, Tomioka Ryota, and Süsstrunk Sabine. 2020 · 2020
Cited alongside, same era.
Contrastive multi-view representation learning on graphs. In International Conference on Machine Learning . PMLR, 4116–4126
Kaveh Hassani and Amir Hosein Khasahmadi. 2020 · 2020
Cited alongside, same era.
Later among the works it cites.
GraphGT: Machine Learning Datasets for Deep Graph Generation and Transformation
Yuanqi Du, Shiyu Wang, Xiaojie Guo, Hengning Cao, Shujie Hu, Junji Jiang, Aishwarya Varala, Abhinav Angirekula, and Liang Zhao. 2021 · 2021
Later among the works it cites.
Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning
Ming Jin, Yizhen Zheng, Yuan-Fang Li, Chen Gong, Chuan Zhou, and Shirui Pan. 2021 · 2021
Later among the works it cites.
Towards robust graph contrastive learning
Nikola Jovanović, Zhao Meng, Lukas Faber, and Roger Wattenhofer. 2021 · 2021
Later among the works it cites.
Contrastive Learning with Hard Negative Samples. In International Conference on Learning Representations
Joshua David Robinson, Ching-Yao Chuang, Suvrit Sra, and Stefanie Jegelka. 2021 · 2021
Later among the works it cites.
MoCL: Contrastive Learning on Molecular Graphs with Multi-level Domain Knowledge
Mengying Sun, Jing Xing, Huijun Wang, Bin Chen, and Jiayu Zhou. 2021 · 2021
Later among the works it cites.
Co-learning: Learning from noisy labels with self-supervision. In Proceedings of the 29th ACM International Conference on Multimedia . 1405–1413
Cheng Tan, Jun Xia, Lirong Wu, and Stan Z Li. 2021 · 2021
Later among the works it cites.
Bootstrapped Representation Learning on Graphs. In ICLR 2021 Workshop on Geometrical and Topological Representation Learning
Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Remi Munos, Petar Veličković, and Michal Valko. 2021 · 2021
Later among the works it cites.
Debiased Graph Contrastive Learning
Jun Xia, Lirong Wu, Jintao Chen, Ge Wang, and Stan Z. Li. 2021b · 2021
Later among the works it cites.
Graph Contrastive Learning Automated
Yuning You, Tianlong Chen, Yang Shen, and Zhangyang Wang. 2021 · 2021
Later among the works it cites.
OT Cleaner: Label Correction as Optimal Transport
Jun Xia, Cheng Tan, Lirong Wu, Yongjie Xu, and Stan Z Li. 2022 · 2022
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Towards Effective and Generalizable Fine-tuning for Pre-trained Molecular Graph Models
JUN XIA, Jiangbin Zheng, Cheng Tan, Ge Wang, and Stan Z Li. 2022 · 2022
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A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications
Jun Xia, Yanqiao Zhu, Yuanqi Du, and Stan Z Li. 2022 · 2022
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Using Context-to-Vector with Graph Retrofitting
Jiangbin Zheng, Yile Wang, Ge Wang, Jun Xia, Yufei Huang, Guojiang Zhao, Yue Zhang, and Stan Z. Li. 2022 · 2022
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Graph Contrastive Learning with Adaptive Augmentation
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