Fetching the paper…
Reading the bibliography…
Graph Prompt Learning (GPL) has been introduced as a promising approach that uses prompts to adapt pre-trained GNN models to specific downstream tasks without requiring fine-tuning of the entire model.
Singular value decomposition (SVD) and generalized singular value decomposition
Hervé Abdi. 2007 · 2007
Earlier work this paper cites.
Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. 2008 · 2008
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks. In ICML . PMLR, 1126–1135
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017 · 2017
Earlier work this paper cites.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Earlier work this paper cites.
Semi-Supervised Classification with Graph Convolutional Networks. In ICLR
Thomas N. Kipf and Max Welling. 2017 · 2017
Earlier work this paper cites.
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017 · 2017
Earlier work this paper cites.
ProG: A Graph Prompt Learning Benchmark
Chenyi Zi, Haihong Zhao, Xiangguo Sun, Yiqing Lin, Hong Cheng, and Jia Li. 2024 · 2017
Earlier work this paper cites.
How Powerful are Graph Neural Networks?. In ICLR
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2018 · 2018
Earlier work this paper cites.
Graph convolutional neural networks for web-scale recommender systems. In SIGKDD . 974–983
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec. 2018 · 2018
Earlier work this paper cites.
Certified adversarial robustness via randomized smoothing. In international conference on machine learning . PMLR, 1310–1320
Jeremy Cohen, Elan Rosenfeld, and Zico Kolter. 2019 · 2019
Earlier work this paper cites.
Graph neural networks for social recommendation. In WWW . 417–426
Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. 2019 · 2019
Earlier work this paper cites.
A Semi-supervised Graph Attentive Network for Financial Fraud Detection. In ICDM . 598–607
Daixin Wang, Jianbin Lin, Peng Cui, Quanhui Jia, Zhen Wang, Yanming Fang, Quan Yu, Jun Zhou, Shuang Yang, and Yuan Qi. 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.
AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts. In Empirical Methods in Natural Language Processing (EMNLP)
Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2020
Earlier work this paper 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. 2020a · 2020
Earlier work this paper cites.
Graph contrastive learning with augmentations
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020b · 2020
Earlier work this paper cites.
Making Pre-trained Language Models Better Few-shot Learners. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Tianyu Gao, Adam Fisch, and Danqi Chen. 2021 · 2021
Earlier work this paper cites.
Overcoming catastrophic forgetting in graph neural networks. In Proceedings of the AAAI conference on artificial intelligence , Vol. 35. 8653–8661
Huihui Liu, Yiding Yang, and Xinchao Wang. 2021 · 2021
Earlier work this paper cites.
Graph backdoor. In 30th USENIX Security Symposium (USENIX Security 21) . 1523–1540
Zhaohan Xi, Ren Pang, Shouling Ji, and Ting Wang. 2021 · 2021
Earlier work this paper cites.
Backdoor attacks to graph neural networks. In Proceedings of the 26th ACM Symposium on Access Control Models and Technologies . 15–26
Zaixi Zhang, Jinyuan Jia, Binghui Wang, and Neil Zhenqiang Gong. 2021 · 2021
Earlier work this paper cites.
Certified neural network watermarks with randomized smoothing. In International Conference on Machine Learning . PMLR, 1450–1465
Arpit Bansal, Ping-yeh Chiang, Michael J Curry, Rajiv Jain, Curtis Wigington, Varun Manjunatha, John P Dickerson, and Tom Goldstein. 2022 · 2022
Earlier work this paper cites.
Graph-augmented normalizing flows for anomaly detection of multiple time series
Enyan Dai and Jie Chen. 2022 · 2022
Cited alongside, same era.
Towards prototype-based self-explainable graph neural network
Enyan Dai and Suhang Wang. 2022 · 2022
Cited alongside, same era.
Prompt tuning for graph neural networks
Taoran Fang, Yunchao Mercer Zhang, Yang Yang, and Chunping Wang. 2022 · 2022
Cited alongside, same era.
Fine-tuning is all you need to mitigate backdoor attacks
Zeyang Sha, Xinlei He, Pascal Berrang, Mathias Humbert, and Yang Zhang. 2022 · 2022
Cited alongside, same era.
Gppt: Graph pre-training and prompt tuning to generalize graph neural networks. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 1717–1727
Mingchen Sun, Kaixiong Zhou, Xin He, Ying Wang, and Xin Wang. 2022 · 2022
Graph and geometry generative modeling for drug discovery. In SIGKDD . 5833–5834
Minkai Xu, Meng Liu, Wengong Jin, Shuiwang Ji, Jure Leskovec, and Stefano Ermon. 2023 · 2023
Later among the works it cites.
Graph contrastive backdoor attacks. In International Conference on Machine Learning . PMLR, 40888–40910
Hangfan Zhang, Jinghui Chen, Lu Lin, Jinyuan Jia, and Dinghao Wu. 2023 · 2023
Later among the works it cites.
Universal prompt tuning for graph neural networks
Taoran Fang, Yunchao Zhang, Yang Yang, Chunping Wang, and Lei Chen. 2024b · 2024
Closest in time.
Adversarial Robustness in Graph Neural Networks: Recent Advances and New Frontier. In 2024 IEEE 11th International Conference on Data Science and Advanced Analytics (DSAA) . IEEE, 1–2
Zhichao Hou, Minhua Lin, MohamadAli Torkamani, Suhang Wang, and Xiaorui Liu. 2024 · 2024
Closest in time.
Prodigy: Enabling in-context learning over graphs
Qian Huang, Hongyu Ren, Peng Chen, Gregor Kržmanc, Daniel Zeng, Percy S Liang, and Jure Leskovec. 2024 · 2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Molecular contrastive learning of representations via graph neural networks
Yuyang Wang, Jianren Wang, Zhonglin Cao, and Amir Barati Farimani. 2022 · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
Cited alongside, same era.
Simgrace: A simple framework for graph contrastive learning without data augmentation. In Proceedings of the ACM Web Conference 2022 . 1070–1079
Jun Xia, Lirong Wu, Jintao Chen, Bozhen Hu, and Stan Z Li. 2022 · 2022
Cited alongside, same era.
Ultra-dp: Unifying graph pre-training with multi-task graph dual prompt
Mouxiang Chen, Zemin Liu, Chenghao Liu, Jundong Li, Qiheng Mao, and Jianling Sun. 2023 · 2023
Cited alongside, same era.
Unnoticeable backdoor attacks on graph neural networks. In Proceedings of the ACM Web Conference 2023 . 2263–2273
Enyan Dai, Minhua Lin, Xiang Zhang, and Suhang Wang. 2023 · 2023
Cited alongside, same era.
Hierarchical local-global transformer for temporal sentence grounding
Xiang Fang, Daizong Liu, Pan Zhou, Zichuan Xu, and Ruixuan Li. 2023b · 2023
Cited alongside, same era.
Enhancing graph neural networks with structure-based prompt
Qingqing Ge, Zeyuan Zhao, Yiding Liu, Anfeng Cheng, Xiang Li, Shuaiqiang Wang, and Dawei Yin. 2023 · 2023
Cited alongside, same era.
Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-Based Similarity. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 38. 12456–12465
O-Joun Lee et al · 2024
Closest in time.
Zerog: Investigating cross-dataset zero-shot transferability in graphs. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 1725–1735
Yuhan Li, Peisong Wang, Zhixun Li, Jeffrey Xu Yu, and Jia Li. 2024 · 2024
Closest in time.
A survey of knowledge graph reasoning on graph types: Static, dynamic, and multi-modal
Ke Liang, Lingyuan Meng, Meng Liu, Yue Liu, Wenxuan Tu, Siwei Wang, Sihang Zhou, Xinwang Liu, Fuchun Sun, and Kunlun He. 2024a · 2024
Closest in time.
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
Closest in time.
Does few-shot learning suffer from backdoor attacks?. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 38. 19893–19901
Xinwei Liu, Xiaojun Jia, Jindong Gu, Yuan Xun, Siyuan Liang, and Xiaochun Cao. 2024 · 2024
Closest in time.
Cross-Context Backdoor Attacks against Graph Prompt Learning. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Xiaoting Lyu, Yufei Han, Wei Wang, Hangwei Qian, Ivor Tsang, and Xiangliang Zhang. 2024 · 2024
Closest in time.
Hetgpt: Haranessing the power of prompt tuning in pre-trained heterogeneous graph neural networks. In Proceedings of the ACM on Web Conference 2024 . 1015–1023
Yihong Ma, Ning Yan, Jiayu Li, Masood Mortazavi, and Nitesh V Chawla. 2024 · 2024
Closest in time.
Krait: A Backdoor Attack Against Graph Prompt Tuning
Ying Song, Rita Singh, and Balaji Palanisamy. 2024 · 2024
Closest in time.
Efficient, direct, and restricted black-box graph evasion attacks to any-layer graph neural networks via influence function. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining . 693–701
Binghui Wang, Minhua Lin, Tianxiang Zhou, Pan Zhou, Ang Li, Meng Pang, Hai Li, and Yiran Chen. 2024 · 2024
Closest in time.
Llm and gnn are complementary: Distilling llm for multimodal graph learning
Junjie Xu, Zongyu Wu, Minhua Lin, Xiang Zhang, and Suhang Wang. 2024 · 2024
Closest in time.
Generalized graph prompt: Toward a unification of pre-training and downstream tasks on graphs
Xingtong Yu, Zhenghao Liu, Yuan Fang, Zemin Liu, Sihong Chen, and Xinming Zhang. 2024b · 2024
Closest in time.
MultiGPrompt for Multi-Task Pre-Training and Prompting on Graphs. In The Web Conference 2024
Xingtong Yu, Chang Zhou, Yuan Fang, and Xinming Zhang. 2024c · 2024
Closest in time.
All in one and one for all: A simple yet effective method towards cross-domain graph pretraining. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 4443–4454
Haihong Zhao, Aochuan Chen, Xiangguo Sun, Hong Cheng, and Jia Li. 2024 · 2024
Closest in time.
Fairness-aware prompt tuning for graph neural networks. In Proceedings of the ACM on Web Conference 2025 . 3586–3597
Zhengpin Li, Minhua Lin, Jian Wang, and Suhang Wang. 2025 · 2025
Closest in time.
Robustness Inspired Graph Backdoor Defense. In The Thirteenth International Conference on Learning Representations
Zhiwei Zhang, Minhua Lin, Junjie Xu, Zongyu Wu, Enyan Dai, and Suhang Wang. 2025 · 2025
Closest in time.