Graph Prompt Learning

Latest papers

9 papers2 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Signed Graph Pre-Training and Prompt Learning
Sep 22, 2026Zihan Mei, Rong Pan, Yuzhou Chen +1Prompt Learning Graph Representation Learning
Arizona State University · University of California, Riverside
Preserving Geometric Integrity in Graph Prompting via Measure-Constrained Optimal Transport
Sep 20, 2026Xiangyu Wang, Shuo Wang, Ruiyi Fang +1Graph Neural Networks Prompt Learning
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China · Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China · Department of Computer Science, Western University, Canada
Task-Specific Prompt with Global Context for Multi-Task Graph Pre-Training
Aug 30, 2026Zhiyang Qiu, Yangtao Wang, Xiaocui Li +3Graph Prompt Learning Prompt Learning
School of Computer Science and Cyber Engineering, Guangzhou University, Guang Zhou, 510006, Guangdong, China · Hunan University of Technology and Business, Changsha, 410205, Hunan, China
Let Prompts Bridge Defense Knowledge: Transferable Graph Purification via Vulnerability-Aware GPL
Aug 29, 2026Shuomin Xue, Jingyuan Li, Ju Jia +2Graph Prompt Learning Graph Transfer Learning
Southeast University Nanjing, China · Nanyang Technological University Singapore, Singapore
VaLiDRec: Variable-Length LLM-Aligned Semantic IDs for Generative Recommendation
Jul 28, 2026Shutong Qiao, Wei Yuan, Tong Chen +3Semantic ID Recommendation Graph Prompt Learning
University of Queensland Brisbane, Australia · Computer Network Information Center, Chinese Academy of Sciences Beijing, China · Griffith University Gold Coast, Australia
Model Agnostic Graph Prompt Learning for Crystal Property Prediction
Jul 9, 2026Shrimon Mukherjee, Kishalay Das, Partha Basuchowdhuri +2Graph Neural Networks Prompt Learning
School of Mathematical & Computational Sciences, Indian Association for the Cultivation of Science, Kolkata, India · Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, India
Learning the Interaction Prior for Protein-Protein Interaction Prediction: A Model-Agnostic Approach
May 11, 2026Ziqi Gao, Chenyi Zi, Zijing Liu +3Prompt Learning Graph Representation Learning
Tsinghua University · The Hong Kong University of Science and Technology (Guangzhou) · IDEA Research +1
Unified Graph Prompt Learning via Low-Rank Graph Message Prompting
Apr 13, 2026Beibei Wang, Bo Jiang, Ziyan Zhang +1Graph Prompt Learning Prompt Learning
School of Computer Science and Technology, Anhui University, Hefei 230601, China. · State Key Laboratory of Opto-Electronic Information Acquisition and Protection Technology, School of Computer Science and Technology, Anhui University, Hefei 230601, China.
From Subtle to Significant: Prompt-Driven Self-Improving Optimization in Test-Time Graph OOD Detection
Feb 19, 2026Luzhi Wang, Xuanshuo Fu, He Zhang +3Graph Prompt Learning OOD Detection
College of Artificial Intelligence, Dalian Maritime University · Computer Vision Center, Universitat Aut`onoma de Barcelona · School of Computing Technologies, RMIT University +1

Sep 22, 2026J/K move · Enter open · S save

Signed Graph Pre-Training and Prompt Learning

Zihan Mei, Rong Pan, Yuzhou Chen, Yixuan He

Arizona State University · University of California, Riverside

Prompt Learning Graph Representation Learning Graph Prompt Learning Graph Transfer Learning Transfer Learning Graph Foundation Models+1

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Signed graphs arise in trust--distrust networks, financial correlation systems, biological interaction graphs, and many other domains in which edges can be positive or negative and may also be directed. While signed graph neural networks have improved task-specific learning, graph transfer learning on signed graphs remains underdeveloped. In this paper, we introduce TopoSIGN, a pioneer topology-guided graph pre-training and prompt learning framework for signed graphs. TopoSIGN combines a structural encoder built on the magnetic signed Laplacian with a novel persistent-homology branch that summarizes signed topology through Dowker-complex persistence images. The fused embeddings are then transferred to a prompt learning function. Experimental results on synthetic and real-world datasets demonstrate the efficacy of TopoSIGN in extracting useful structural information in signed graphs, as well as the adaptability and flexibility of the proposed general framework.