4 papers
Beyond One-Size-Fits-All: Adaptive Subgraph Denoising for Zero-Shot Graph Learning with Large Language Models
Fengzhi Li, Liang Zhang, Yuan Zuo +5
Graph-based tasks in the zero-shot setting remain a significant challenge due to data scarcity and the inability of traditional Graph Neural Networks (GNNs) to generalize to unseen…
UniGTE: Unified Graph-Text Encoding for Zero-Shot Generalization across Graph Tasks and Domains
Duo Wang, Yuan Zuo, Guangyue Lu +1
Generalizing to unseen graph tasks without task-specific supervision is challenging: conventional graph neural networks are typically tied to a fixed label space, while large langu…
Graph-R1: Incentivizing the Zero-Shot Graph Learning Capability in LLMs via Explicit Reasoning
Yicong Wu, Guangyue Lu, Yuan Zuo +2
Generalizing to unseen graph tasks without task-pecific supervision remains challenging. Graph Neural Networks (GNNs) are limited by fixed label spaces, while Large Language Models…
LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings
Duo Wang, Yuan Zuo, Fengzhi Li +1
Zero-shot graph machine learning, especially with graph neural networks (GNNs), has garnered significant interest due to the challenge of scarce labeled data. While methods like se…