A Unified Graph Selective Prompt Learning for Graph Neural Networks
Fuente:
arXiv
Saved in:
| Main Authors: | Jiang, Bo, Wu, Hao, Zhang, Ziyan, Wang, Beibei, Tang, Jin |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Unified Graph Prompt Learning via Low-Rank Graph Message Prompting
by: Wang, Beibei, et al.
Published: (2026)
by: Wang, Beibei, et al.
Published: (2026)
Graph Structure Prompt Learning: A Novel Methodology to Improve Performance of Graph Neural Networks
by: Huang, Zhenhua, et al.
Published: (2024)
by: Huang, Zhenhua, 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)
Forward Learning of Graph Neural Networks
by: Park, Namyong, et al.
Published: (2024)
by: Park, Namyong, et al.
Published: (2024)
Conditional Local Feature Encoding for Graph Neural Networks
by: Wang, Yongze, et al.
Published: (2024)
by: Wang, Yongze, et al.
Published: (2024)
Unifying Generation and Prediction on Graphs with Latent Graph Diffusion
by: Zhou, Cai, et al.
Published: (2024)
by: Zhou, Cai, 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)
GraphMU: Repairing Robustness of Graph Neural Networks via Machine Unlearning
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)
Demystifying Oversmoothing in Attention-Based Graph Neural Networks
by: Wu, Xinyi, et al.
Published: (2023)
by: Wu, Xinyi, et al.
Published: (2023)
Prompt Learning on Temporal Interaction Graphs
by: Chen, Xi, et al.
Published: (2024)
by: Chen, Xi, et al.
Published: (2024)
Optimizing Luxury Vehicle Dealership Networks: A Graph Neural Network Approach to Site Selection
by: Carocci, Luca Silvano, et al.
Published: (2024)
by: Carocci, Luca Silvano, 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)
Incorporating Heterophily into Graph Neural Networks for Graph Classification
by: Yang, Jiayi, et al.
Published: (2022)
by: Yang, Jiayi, et al.
Published: (2022)
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)
Towards Lightweight Graph Neural Network Search with Curriculum Graph Sparsification
by: Xie, Beini, et al.
Published: (2024)
by: Xie, Beini, 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)
Graph Neural Networks with Diverse Spectral Filtering
by: Guo, Jingwei, et al.
Published: (2023)
by: Guo, Jingwei, 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)
Understanding the Robustness of Graph Neural Networks against Adversarial Attacks
by: Wu, Tao, et al.
Published: (2024)
by: Wu, Tao, 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)
Hybrid Graph: A Unified Graph Representation with Datasets and Benchmarks for Complex Graphs
by: Li, Zehui, et al.
Published: (2023)
by: Li, Zehui, et al.
Published: (2023)
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)
A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt Learning
by: Yu, Xingtong, et al.
Published: (2024)
by: Yu, Xingtong, et al.
Published: (2024)
Learn from Heterophily: Heterophilous Information-enhanced Graph Neural Network
by: Zheng, Yilun, et al.
Published: (2024)
by: Zheng, Yilun, 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)
GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection
by: Park, Namyong, et al.
Published: (2024)
by: Park, Namyong, et al.
Published: (2024)
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)
Heterogeneous Graph Neural Network on Semantic Tree
by: Guan, Mingyu, et al.
Published: (2024)
by: Guan, Mingyu, 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)
Epidemiology-informed Graph Neural Network for Heterogeneity-aware Epidemic Forecasting
by: Zheng, Yufan, et al.
Published: (2024)
by: Zheng, Yufan, 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)
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)
Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure
by: Sato, Ryoma
Published: (2023)
by: Sato, Ryoma
Published: (2023)
GraphPrompter: Multi-stage Adaptive Prompt Optimization for Graph In-Context Learning
by: Lv, Rui, et al.
Published: (2025)
by: Lv, Rui, et al.
Published: (2025)
A Graph Neural Network deep-dive into successful counterattacks
by: Bekkers, Joris, et al.
Published: (2024)
by: Bekkers, Joris, et al.
Published: (2024)
PropEnc: A Property Encoder for Graph Neural Networks
by: Said, Anwar, et al.
Published: (2024)
by: Said, Anwar, et al.
Published: (2024)
HeteGraph-Mamba: Heterogeneous Graph Learning via Selective State Space Model
by: Pan, Zhenyu, et al.
Published: (2024)
by: Pan, Zhenyu, et al.
Published: (2024)
Data-centric Graph Learning: A Survey
by: Guo, Yuxin, et al.
Published: (2023)
by: Guo, Yuxin, et al.
Published: (2023)
Similar Items
-
Unified Graph Prompt Learning via Low-Rank Graph Message Prompting
by: Wang, Beibei, et al.
Published: (2026) -
Graph Structure Prompt Learning: A Novel Methodology to Improve Performance of Graph Neural Networks
by: Huang, Zhenhua, et al.
Published: (2024) -
Feature Selection and Extraction for Graph Neural Networks
by: Acharya, Deepak Bhaskar, et al.
Published: (2019) -
Forward Learning of Graph Neural Networks
by: Park, Namyong, et al.
Published: (2024) -
Conditional Local Feature Encoding for Graph Neural Networks
by: Wang, Yongze, et al.
Published: (2024)