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Pretraining on Graph Neural Networks (GNNs) has shown great power in facilitating various downstream tasks.
On random graphs I
P ERDdS and A R&wi. 1959 · 1959
Earlier work this paper cites.
ZINC 15 – Ligand Discovery for Everyone
Teague Sterling and John J. Irwin. 2015 · 2015
Earlier work this paper cites.
Embedding watermarks into deep neural networks. In ICMR . 269–277
Yusuke Uchida, Yuki Nagai, Shigeyuki Sakazawa, and Shin’ichi Satoh. 2017 · 2017
Earlier work this paper cites.
To prune, or not to prune: exploring the efficacy of pruning for model compression
Michael Zhu and Suyog Gupta. 2017 · 2017
Earlier work this paper cites.
Turning your weakness into a strength: Watermarking deep neural networks by backdooring. In USENIX Security 18 . 1615–1631
Yossi Adi, Carsten Baum, Moustapha Cisse, Benny Pinkas, and Joseph Keshet. 2018 · 2018
Earlier work this paper cites.
Performance comparison of contemporary DNN watermarking techniques
Huili Chen, Bita Darvish Rouhani, Xinwei Fan, Osman Cihan Kilinc, and Farinaz Koushanfar. 2018 · 2018
Earlier work this paper cites.
On first-order meta-learning algorithms
Alex Nichol, Joshua Achiam, and John Schulman. 2018 · 2018
Earlier work this paper cites.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018 · 2018
Earlier work this paper cites.
Deepsigns: A generic watermarking framework for ip protection of deep learning models
Bita Darvish Rouhani, Huili Chen, and Farinaz Koushanfar. 2018 · 2018
Earlier work this paper cites.
Lipschitz regularity of deep neural networks: analysis and efficient estimation
Aladin Virmaux and Kevin Scaman. 2018 · 2018
Earlier work this paper cites.
MoleculeNet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande. 2018 · 2018
Earlier work this paper cites.
Protecting intellectual property of deep neural networks with watermarking. In ASIACCS . 159–172
Jialong Zhang, Zhongshu Gu, Jiyong Jang, Hui Wu, Marc Ph Stoecklin, Heqing Huang, and Ian Molloy. 2018 · 2018
Earlier work this paper cites.
Certified adversarial robustness via randomized smoothing. In ICML . PMLR, 1310–1320
Jeremy Cohen, Elan Rosenfeld, and Zico Kolter. 2019 · 2019
Earlier work this paper cites.
Strategies for Pre-training Graph Neural Networks. In International Conference on Learning Representations
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec. 2020 · 2020
Earlier work this paper cites.
Gcc: Graph contrastive coding for graph neural network pre-training. In SIGKDD . 1150–1160
Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, and Jie Tang. 2020 · 2020
Earlier work this paper cites.
Understanding Isomorphism Bias in Graph Data Sets
Ivanov Sergey, Sviridov Sergey, and Evgeny Burnaev. 2020 · 2020
Cited alongside, same era.
Graph contrastive learning with augmentations
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020 · 2020
Cited alongside, same era.
Backdoor pre-trained models can transfer to all
Lujia Shen, Shouling Ji, Xuhong Zhang, Jinfeng Li, Jing Chen, Jie Shi, Chengfang Fang, Jianwei Yin, and Ting Wang. 2021 · 2021
Cited alongside, same era.
Robust watermarking for deep neural networks via bi-level optimization. In ICCV . 14841–14850
Peng Yang, Yingjie Lao, and Ping Li. 2021 · 2021
Cited alongside, same era.
Watermarking graph neural networks by random graphs. In ISDFS . IEEE, 1–6
Xiangyu Zhao, Hanzhou Wu, and Xinpeng Zhang. 2021 · 2021
Cited alongside, same era.
Protein representation learning by geometric structure pretraining
Zuobai Zhang, Minghao Xu, Arian Jamasb, Vijil Chenthamarakshan, Aurelie Lozano, Payel Das, and Jian Tang. 2022 · 2022
Later among the works it cites.
Certifiably robust graph contrastive learning
Minhua Lin, Teng Xiao, Enyan Dai, Xiang Zhang, and Suhang Wang. 2023 · 2023
Later among the works it cites.
Wenjun Peng, Jingwei Yi, Fangzhao Wu, Shangxi Wu, Bin Zhu, Lingjuan Lyu, Binxing Jiao, Tong Xu, Guangzhong Sun, and Xing Xie. 2023 · 2023
Later among the works it cites.
Deep Intellectual Property: A Survey
Yuchen Sun, Tianpeng Liu, Panhe Hu, Qing Liao, Shouling Ji, Nenghai Yu, Deke Guo, and Li Liu. 2023b · 2023
Later among the works it cites.
Watermarking graph neural networks based on backdoor attacks. In EuroS&P . IEEE, 1179–1197
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Yanqiao Zhu, Yichen Xu, Qiang Liu, and Shu Wu. 2021 · 2021
Cited alongside, same era.
Certified Neural Network Watermarks with Randomized Smoothing. In ICML . 1450–1465
Arpit Bansal, Ping-Yeh Chiang, Michael J Curry, Rajiv Jain, Curtis Wigington, Varun Manjunatha, John P Dickerson, and Tom Goldstein. 2022 · 2022
Cited alongside, same era.
SSLGuard: A watermarking scheme for self-supervised learning pre-trained encoders. In CCS . 579–593
Tianshuo Cong, Xinlei He, and Yang Zhang. 2022 · 2022
Cited alongside, same era.
Graph-augmented normalizing flows for anomaly detection of multiple time series
Enyan Dai and Jie Chen. 2022 · 2022
Cited alongside, same era.
Towards robust graph neural networks for noisy graphs with sparse labels. In WSDM . 181–191
Enyan Dai, Wei Jin, Hui Liu, and Suhang Wang. 2022 · 2022
Cited alongside, same era.
Learning fair graph neural networks with limited and private sensitive attribute information
Enyan Dai and Suhang Wang. 2022 · 2022
Cited alongside, same era.
Graphmae: Self-supervised masked graph autoencoders. In SIGKDD . 594–604
Zhenyu Hou, Xiao Liu, Yukuo Cen, Yuxiao Dong, Hongxia Yang, Chunjie Wang, and Jie Tang. 2022 · 2022
Cited alongside, same era.
Jing Xu, Stefanos Koffas, Oğuzhan Ersoy, and Stjepan Picek. 2023 · 2023
Later among the works it cites.
Graph contrastive backdoor attacks. In ICML . PMLR, 40888–40910
Hangfan Zhang, Jinghui Chen, Lu Lin, Jinyuan Jia, and Dinghao Wu. 2023 · 2023
Later among the works it cites.
A comprehensive survey on trustworthy graph neural networks: Privacy, robustness, fairness, and explainability
Enyan Dai, Tianxiang Zhao, Huaisheng Zhu, Junjie Xu, Zhimeng Guo, Hui Liu, Jiliang Tang, and Suhang Wang. 2024 · 2024
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Adversarial Robustness in Graph Neural Networks: Recent Advances and New Frontier. In DSAA . IEEE, 1–2
Zhichao Hou, Minhua Lin, MohamadAli Torkamani, Suhang Wang, and Xiaorui Liu. 2024 · 2024
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Decoding Time Series with LLMs: A Multi-Agent Framework for Cross-Domain Annotation
Minhua Lin, Zhengzhang Chen, Yanchi Liu, Xujiang Zhao, Zongyu Wu, Junxiang Wang, Xiang Zhang, Suhang Wang, and Haifeng Chen. 2024 · 2024
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Llm and gnn are complementary: Distilling llm for multimodal graph learning
Junjie Xu, Zongyu Wu, Minhua Lin, Xiang Zhang, and Suhang Wang. 2024 · 2024
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Towards prototype-based self-explainable graph neural network
Enyan Dai and Suhang Wang. 2025 · 2025
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UniZyme: A Unified Protein Cleavage Site Predictor Enhanced with Enzyme Active-Site Knowledge
Chenao Li, Shuo Yan, and Enyan Dai. 2025 · 2025
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