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Does Graph Prompt Work? A Data Operation Perspective with Theoretical Analysis
v1v2 (latest)

Does Graph Prompt Work? A Data Operation Perspective with Theoretical Analysis

2 October 2024
Qunzhong Wang
Xiangguo Sun
Hong Cheng
ArXiv (abs)PDFHTML

Papers citing "Does Graph Prompt Work? A Data Operation Perspective with Theoretical Analysis"

27 / 27 papers shown
Cross-Paradigm Graph Backdoor Attacks with Promptable Subgraph Triggers
Cross-Paradigm Graph Backdoor Attacks with Promptable Subgraph Triggers
Dongyi Liu
Jiangtong Li
Dawei Cheng
Changjun Jiang
AAML
116
0
0
26 Oct 2025
Graph Prompting for Graph Learning Models: Recent Advances and Future Directions
Xingbo Fu
Zehong Wang
Zihan Chen
Jiazheng Li
Yaochen Zhu
Zhenyu Lei
Cong Shen
Yanfang Ye
Chuxu Zhang
Jundong Li
AI4CEVLM
211
3
0
10 Jun 2025
GCAL: Adapting Graph Models to Evolving Domain Shifts
GCAL: Adapting Graph Models to Evolving Domain Shifts
Ziyue Qiao
Qianyi Cai
Hao Dong
Jiawei Gu
Pengyang Wang
Meng Xiao
Xiao Luo
Hui Xiong
CLL
271
3
0
22 May 2025
Boundary Prompting: Elastic Urban Region Representation via Graph-based Spatial Tokenization
Haojia Zhu
Jiahui Jin
Dong Kan
Rouxi Shen
Ruize Wang
Xiangguo Sun
Jinghui Zhang
296
0
0
11 Mar 2025
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt Tuning
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningKnowledge Discovery and Data Mining (KDD), 2024
Jiapeng Zhu
Zichen Ding
Jianxiang Yu
Jiaqi Tan
Xiang Li
Weining Qian
OffRL
558
11
0
20 Jan 2025
Reliable and Compact Graph Fine-tuning via GraphSparse Prompting
Reliable and Compact Graph Fine-tuning via GraphSparse Prompting
Bo Jiang
Hao Wu
Beibei Wang
Jin Tang
Bin Luo
220
2
0
29 Oct 2024
Adaptive Graph Integration for Cross-Domain Recommendation via Heterogeneous Graph Coordinators
Adaptive Graph Integration for Cross-Domain Recommendation via Heterogeneous Graph CoordinatorsAnnual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2024
Hengyu Zhang
Chunxu Shen
Xiangguo Sun
Jie Tan
Yu Rong
Chengzhi Piao
Hong Cheng
Lingling Yi
224
0
0
15 Oct 2024
Urban Region Pre-training and Prompting: A Graph-based Approach
Urban Region Pre-training and Prompting: A Graph-based ApproachKnowledge Discovery and Data Mining (KDD), 2024
Jiahui Jin
Yifan Song
Dong Kan
Haojia Zhu
Xiangguo Sun
Zhicheng Li
Xigang Sun
Jinghui Zhang
AI4TSAI4CE
496
6
0
12 Aug 2024
ProG: A Graph Prompt Learning Benchmark
ProG: A Graph Prompt Learning BenchmarkNeural Information Processing Systems (NeurIPS), 2024
Chenyi Zi
Haihong Zhao
Xiangguo Sun
Yiqing Lin
Hong Cheng
Jia Li
376
28
0
08 Jun 2024
All in One and One for All: A Simple yet Effective Method towards
  Cross-domain Graph Pretraining
All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph Pretraining
Haihong Zhao
Chenyi Zi
Xiangguo Sun
Hong Cheng
Jia Li
317
75
0
15 Feb 2024
Prompt Learning on Temporal Interaction Graphs
Prompt Learning on Temporal Interaction Graphs
Xi Chen
Siwei Zhang
Yun Xiong
Xixi Wu
Jiawei Zhang
Xiangguo Sun
Yao Zhang
Feng Zhao
Yulin Kang
AI4CE
275
13
0
09 Feb 2024
GraphPro: Graph Pre-training and Prompt Learning for Recommendation
GraphPro: Graph Pre-training and Prompt Learning for RecommendationThe Web Conference (WWW), 2023
Yuhao Yang
Lianghao Xia
Da Luo
Kangyi Lin
Chao Huang
AI4CE
458
35
0
28 Nov 2023
Graph Prompt Learning: A Comprehensive Survey and Beyond
Graph Prompt Learning: A Comprehensive Survey and Beyond
Xiangguo Sun
Jiawen Zhang
Xixi Wu
Hong Cheng
Yun Xiong
Jia Li
901
85
0
28 Nov 2023
HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks
HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural NetworksThe Web Conference (WWW), 2023
Yihong Ma
Ning Yan
Jiayu Li
Masood S. Mortazavi
Nitesh Chawla
490
41
0
23 Oct 2023
GraphControl: Adding Conditional Control to Universal Graph Pre-trained
  Models for Graph Domain Transfer Learning
GraphControl: Adding Conditional Control to Universal Graph Pre-trained Models for Graph Domain Transfer LearningThe Web Conference (WWW), 2023
Yun Zhu
Yaoke Wang
Haizhou Shi
Zhenshuo Zhang
Dian Jiao
Siliang Tang
AI4CE
478
35
0
11 Oct 2023
All in One: Multi-task Prompting for Graph Neural Networks
All in One: Multi-task Prompting for Graph Neural NetworksKnowledge Discovery and Data Mining (KDD), 2023
Xiangguo Sun
Hongtao Cheng
Jia Li
Bo Liu
Jihong Guan
LLMAGAI4CE
349
216
0
04 Jul 2023
Virtual Node Tuning for Few-shot Node Classification
Virtual Node Tuning for Few-shot Node ClassificationKnowledge Discovery and Data Mining (KDD), 2023
Zhen Tan
Ruocheng Guo
Kaize Ding
Huan Liu
239
56
0
09 Jun 2023
PRODIGY: Enabling In-context Learning Over Graphs
PRODIGY: Enabling In-context Learning Over GraphsNeural Information Processing Systems (NeurIPS), 2023
Qian Huang
Hongyu Ren
Peng Chen
Gregor Krvzmanc
D. Zeng
Abigail Z. Jacobs
J. Leskovec
270
102
0
21 May 2023
GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural
  Networks
GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural NetworksThe Web Conference (WWW), 2023
Zemin Liu
Xingtong Yu
Yuan Fang
Xinming Zhang
LLMAGAI4CE
451
226
0
16 Feb 2023
MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular
  Representation Learning
MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning
Cameron Diao
Kaixiong Zhou
Zirui Liu
Xiao Huang
Helen Zhou
169
16
0
20 Dec 2022
Universal Prompt Tuning for Graph Neural Networks
Universal Prompt Tuning for Graph Neural NetworksNeural Information Processing Systems (NeurIPS), 2022
Taoran Fang
Yunchao Zhang
Yang Yang
Chunping Wang
Lei Chen
492
97
0
30 Sep 2022
TUDataset: A collection of benchmark datasets for learning with graphs
TUDataset: A collection of benchmark datasets for learning with graphs
Christopher Morris
Nils M. Kriege
Franka Bause
Kristian Kersting
Petra Mutzel
Marion Neumann
546
986
0
16 Jul 2020
Diffusion Improves Graph Learning
Diffusion Improves Graph LearningNeural Information Processing Systems (NeurIPS), 2019
Johannes Klicpera
Stefan Weißenberger
Stephan Günnemann
GNN
1.2K
820
0
28 Oct 2019
Pre-train and Learn: Preserve Global Information for Graph Neural
  Networks
Pre-train and Learn: Preserve Global Information for Graph Neural NetworksJournal of Computational Science and Technology (JCST), 2019
Danhao Zhu
Xinyu Dai
Jiajun Chen
158
26
0
27 Oct 2019
Graph Attention Networks
Graph Attention NetworksInternational Conference on Learning Representations (ICLR), 2017
Petar Velickovic
Guillem Cucurull
Arantxa Casanova
Adriana Romero
Pietro Lio
Yoshua Bengio
GNN
3.8K
24,198
0
30 Oct 2017
Semi-Supervised Classification with Graph Convolutional Networks
Semi-Supervised Classification with Graph Convolutional Networks
Thomas Kipf
Max Welling
GNNSSL
1.8K
32,963
0
09 Sep 2016
On the Expressive Power of Deep Neural Networks
On the Expressive Power of Deep Neural Networks
M. Raghu
Ben Poole
Jon M. Kleinberg
Surya Ganguli
Jascha Narain Sohl-Dickstein
525
853
0
16 Jun 2016
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