Graph Structure Prompt Learning: A Novel Methodology to Improve Performance of Graph Neural Networks
Fuente:
arXiv
Saved in:
| Main Authors: | Huang, Zhenhua, Li, Kunhao, Wang, Shaojie, Jia, Zhaohong, Zhu, Wentao, Mehrotra, Sharad |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
SES: Bridging the Gap Between Explainability and Prediction of Graph Neural Networks
by: Huang, Zhenhua, et al.
Published: (2024)
by: Huang, Zhenhua, et al.
Published: (2024)
A Unified Graph Selective Prompt Learning for Graph Neural Networks
by: Jiang, Bo, et al.
Published: (2024)
by: Jiang, Bo, et al.
Published: (2024)
GraphMU: Repairing Robustness of Graph Neural Networks via Machine Unlearning
by: Wu, Tao, et al.
Published: (2024)
by: Wu, Tao, et al.
Published: (2024)
Understanding the Robustness of Graph Neural Networks against Adversarial Attacks
by: Wu, Tao, et al.
Published: (2024)
by: Wu, Tao, et al.
Published: (2024)
GISExplainer: On Explainability of Graph Neural Networks via Game-theoretic Interaction Subgraphs
by: Xian, Xingping, et al.
Published: (2024)
by: Xian, Xingping, et al.
Published: (2024)
LoRAP: Low-Rank Aggregation Prompting for Quantized Graph Neural Networks Training
by: Liu, Chenyu, et al.
Published: (2026)
by: Liu, Chenyu, et al.
Published: (2026)
Forward Learning of Graph Neural Networks
by: Park, Namyong, et al.
Published: (2024)
by: Park, Namyong, et al.
Published: (2024)
Improving Expressive Power of Spectral Graph Neural Networks with Eigenvalue Correction
by: Lu, Kangkang, et al.
Published: (2024)
by: Lu, Kangkang, et al.
Published: (2024)
Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure
by: Sato, Ryoma
Published: (2023)
by: Sato, Ryoma
Published: (2023)
Graph Neural Networks with Diverse Spectral Filtering
by: Guo, Jingwei, et al.
Published: (2023)
by: Guo, Jingwei, et al.
Published: (2023)
Improving Signed Propagation for Graph Neural Networks in Multi-Class Environments
by: Choi, Yoonhyuk, et al.
Published: (2023)
by: Choi, Yoonhyuk, et al.
Published: (2023)
The Expressive Power of Graph Neural Networks: A Survey
by: Zhang, Bingxu, et al.
Published: (2023)
by: Zhang, Bingxu, et al.
Published: (2023)
Incorporating Heterophily into Graph Neural Networks for Graph Classification
by: Yang, Jiayi, et al.
Published: (2022)
by: Yang, Jiayi, et al.
Published: (2022)
Revisiting the Message Passing in Heterophilous Graph Neural Networks
by: Zheng, Zhuonan, et al.
Published: (2024)
by: Zheng, Zhuonan, et al.
Published: (2024)
Heterophilous Distribution Propagation for Graph Neural Networks
by: Zheng, Zhuonan, et al.
Published: (2024)
by: Zheng, Zhuonan, et al.
Published: (2024)
Towards Graph Prompt Learning: A Survey and Beyond
by: Long, Qingqing, et al.
Published: (2024)
by: Long, Qingqing, et al.
Published: (2024)
Manufacturing Service Capability Prediction with Graph Neural Networks
by: Li, Yunqing, et al.
Published: (2024)
by: Li, Yunqing, et al.
Published: (2024)
Modeling Authorial Style in Urdu Novels Using Character Interaction Graphs and Graph Neural Networks
by: Mujtaba, Hassan, et al.
Published: (2025)
by: Mujtaba, Hassan, et al.
Published: (2025)
What Is Missing In Homophily? Disentangling Graph Homophily For Graph Neural Networks
by: Zheng, Yilun, et al.
Published: (2024)
by: Zheng, Yilun, et al.
Published: (2024)
InfraredGP: Efficient Graph Partitioning via Spectral Graph Neural Networks with Negative Corrections
by: Qin, Meng, et al.
Published: (2025)
by: Qin, Meng, et al.
Published: (2025)
Dynamic Graph with Similarity-Aware Attention Graph Neural Network for Recommender Systems
by: Senapati, Aadarsh, et al.
Published: (2026)
by: Senapati, Aadarsh, et al.
Published: (2026)
GFairHint: Improving Individual Fairness for Graph Neural Networks via Fairness Hint
by: Xu, Paiheng, et al.
Published: (2023)
by: Xu, Paiheng, et al.
Published: (2023)
Learn from Heterophily: Heterophilous Information-enhanced Graph Neural Network
by: Zheng, Yilun, et al.
Published: (2024)
by: Zheng, Yilun, et al.
Published: (2024)
Dynamic Fraud Detection: Integrating Reinforcement Learning into Graph Neural Networks
by: Dong, Yuxin, et al.
Published: (2024)
by: Dong, Yuxin, et al.
Published: (2024)
GNNAnatomy: Rethinking Model-Level Explanations for Graph Neural Networks
by: Lu, Hsiao-Ying, et al.
Published: (2024)
by: Lu, Hsiao-Ying, et al.
Published: (2024)
Disambiguated Node Classification with Graph Neural Networks
by: Zhao, Tianxiang, et al.
Published: (2024)
by: Zhao, Tianxiang, et al.
Published: (2024)
Heterogeneous Graph Neural Network on Semantic Tree
by: Guan, Mingyu, et al.
Published: (2024)
by: Guan, Mingyu, et al.
Published: (2024)
Feature Selection and Extraction for Graph Neural Networks
by: Acharya, Deepak Bhaskar, et al.
Published: (2019)
by: Acharya, Deepak Bhaskar, et al.
Published: (2019)
Towards Lightweight Graph Neural Network Search with Curriculum Graph Sparsification
by: Xie, Beini, et al.
Published: (2024)
by: Xie, Beini, et al.
Published: (2024)
GraphSB: Boosting Imbalanced Node Classification on Graphs through Structural Balance
by: Zhu, Chaofan, et al.
Published: (2025)
by: Zhu, Chaofan, et al.
Published: (2025)
Disentangling, Amplifying, and Debiasing: Learning Disentangled Representations for Fair Graph Neural Networks
by: Lee, Yeon-Chang, et al.
Published: (2024)
by: Lee, Yeon-Chang, et al.
Published: (2024)
Using Time-Aware Graph Neural Networks to Predict Temporal Centralities in Dynamic Graphs
by: Heeg, Franziska, et al.
Published: (2023)
by: Heeg, Franziska, et al.
Published: (2023)
Chi-Square Wavelet Graph Neural Networks for Heterogeneous Graph Anomaly Detection
by: Li, Xiping, et al.
Published: (2025)
by: Li, Xiping, et al.
Published: (2025)
Link Prediction with Physics-Inspired Graph Neural Networks
by: Di Francesco, Andrea Giuseppe, et al.
Published: (2024)
by: Di Francesco, Andrea Giuseppe, et al.
Published: (2024)
Reconciling Attribute and Structural Anomalies for Improved Graph Anomaly Detection
by: Xiao, Chunjing, et al.
Published: (2025)
by: Xiao, Chunjing, et al.
Published: (2025)
Prompt Learning on Temporal Interaction Graphs
by: Chen, Xi, et al.
Published: (2024)
by: Chen, Xi, et al.
Published: (2024)
Self-Attention Empowered Graph Convolutional Network for Structure Learning and Node Embedding
by: Jiang, Mengying, et al.
Published: (2024)
by: Jiang, Mengying, et al.
Published: (2024)
Demystifying Oversmoothing in Attention-Based Graph Neural Networks
by: Wu, Xinyi, et al.
Published: (2023)
by: Wu, Xinyi, et al.
Published: (2023)
Conditional Local Feature Encoding for Graph Neural Networks
by: Wang, Yongze, et al.
Published: (2024)
by: Wang, Yongze, et al.
Published: (2024)
How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing
by: Huang, Keke, et al.
Published: (2024)
by: Huang, Keke, et al.
Published: (2024)
Similar Items
-
SES: Bridging the Gap Between Explainability and Prediction of Graph Neural Networks
by: Huang, Zhenhua, et al.
Published: (2024) -
A Unified Graph Selective Prompt Learning for Graph Neural Networks
by: Jiang, Bo, et al.
Published: (2024) -
GraphMU: Repairing Robustness of Graph Neural Networks via Machine Unlearning
by: Wu, Tao, et al.
Published: (2024) -
Understanding the Robustness of Graph Neural Networks against Adversarial Attacks
by: Wu, Tao, et al.
Published: (2024) -
GISExplainer: On Explainability of Graph Neural Networks via Game-theoretic Interaction Subgraphs
by: Xian, Xingping, et al.
Published: (2024)