Graph Prompting

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8 papers0 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

AtlasULP: Domain-aware Universal Link Prediction via Relation Atlas
Nov 5, 2026Yujing Liu, Yixin Liu, Yu Zheng +4Graph Prompting Link Prediction
School of Information and Communication Technology, Griffith University, Australia · Department of Computer Science and Information Technology, La Trobe University, Australia · School of Computer Science and Technology, Tongji University, China
Spectral Reversal: Counteracting Singular Value Bias for Graph Prompting
Sep 26, 2026Hanxu Yang, Yuhuan Zhao, Xiaodong He +1Graph Prompting Graph Neural Networks
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
Graph Evidence Is Not Enough: Diagnosing Native Decoder Use in Graph-Augmented LLMs
Aug 31, 2026Xiaoyu Guo, Pengcheng Chen, Jiong Yu +3LLM Reasoning with Graphs Graph Prompting
School of Software, Xinjiang University, Urumqi 830002, China · School of Computer Science and Technology, Xinjiang University, Urumqi 830046, China
Dynamic Graph Prompting via Topology-Routed Mixed-Curvature Experts
Aug 6, 2026Quanxin Wang, Xuanting Xie, Bingheng Li +4Link Prediction Few-Shot Learning
University of Electronic Science and Technology of China · 2Michigan State University · 3Singapore Management University +2
Toward a universal foundation model for graph-structured data
Apr 7, 2026Sakib Mostafa, Lei Xing, Md. Tauhidul IslamGraph Transfer Learning Graph Prompting
Department of Radiation Oncology, Stanford University, Stanford, CA, USA · Institute of Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA · Department of Electrical Engineering, Stanford University, Stanford, CA, USA
A Cross-graph Tuning-free GNN Prompting Framework
Apr 1, 2026Yaqi Chen, Shixun Huang, Ryan Twemlow +6Graph Prompting Graph Neural Networks
University of Wollongong, Northfields Avenue, Gwynneville, 2500, NSW, Australia · Wuhan University, No. 299, Luojia Hill, Wuhan, 430072, Hubei, China
Disentangled Graph Prompting for Out-Of-Distribution Detection
Mar 31, 2026Cheng Yang, Yu Hao, Qi Zhang +1Graph Prompting OOD Detection
the Beijing Key Lab of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, China · China Mobile Group Shaanxi Co., Ltd.
Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting
May 22, 2025Peyman Baghershahi, Sourav MedyaSource-Free Domain Adaptation Unsupervised Domain Adaptation
Department of Computer Science University of Illinois Chicago

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AtlasULP: Domain-aware Universal Link Prediction via Relation Atlas

Yujing Liu, Yixin Liu, Yu Zheng, Lianhua Chi+3

School of Information and Communication Technology, Griffith University, Australia · Department of Computer Science and Information Technology, La Trobe University, Australia · School of Computer Science and Technology, Tongji University, China

Graph Prompting Link Prediction

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Link prediction (LP) is a widely applied task in graph learning. Conventional LP approaches typically follow a dataset-specific paradigm, requiring independent training for each graph and incurring high computational and maintenance costs in large-scale applications. Motivated by these limitations, recent work explores Universal Link Prediction (ULP), aiming to enable training-free inference on arbitrary unseen graphs. However, existing methods primarily rely on subgraph sampling strategies to construct universal representations, which are unable to capture higher-order information due to the exponential growth of higher-order neighborhoods. As a result, they often under-perform on graphs where long-range signals are essential, such as biological networks. Moreover, their prediction models are typically domain-agnostic and lack the ability to adapt to different graph domains, leading to limited generalization. To address these challenges, we propose an Atlas-based Universal Link Prediction framework (AtlasULP), which introduces the relation atlas to capture structural associations between node pairs from both global and local perspectives, enabling a comprehensive characterization of connectivity signals across diverse graph domains. Building upon this representation, we develop a domain-guided prompting ULP model, which generates domain-aware prompt tokens from contextual structures and performs prompt-guided in-context prediction for adaptive link inference. Extensive experiments demonstrate that AtlasULP consistently outperforms state-of-the-art methods across diverse graph domains.