1 citations · 1 across the 4 of their papers we have counts for
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Feature to Dynamics: Feature-space to Autoregression strategy for Zero-shot Time Series Forecasting
Yifan Wu, Junjie Wu, Kai Wu +2
Zero-shot time series forecasting aims to predict future values for previously unseen series, requiring models to generalize temporal dynamics beyond the training distribution. Whi…
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…