用大模型预测动态图信号缺失值，提升精度。
LLM-based Online Prediction of Time-varying Graph Signals
- 利用大模型实现邻居与历史估计的跨节点信息传递
- 在风速图信号在线预测中优于传统图滤波算法
- 适合处理部分观测的动态图数据场景
本文提出一种新框架，借助大语言模型（LLMs）预测时变图信号中的缺失值，充分利用空间与时间平滑性。通过将每个缺失节点的邻接节点及其历史估计输入大模型，实现信息聚合与推断。在风速图信号在线预测任务上，该方法相比在线图滤波算法显著提升预测精度，验证了大模型在处理部分观测图信号方面的潜力。
原文摘要 · Abstract (English)
In this paper, we propose a novel framework that leverages large language models (LLMs) for predicting missing values in time-varying graph signals by exploiting spatial and temporal smoothness. We leverage the power of LLM to achieve a message-passing scheme. For each missing node, its neighbors and previous estimates are fed into and processed by LLM to infer the missing observations. Tested on the task of the online prediction of wind-speed graph signals, our model outperforms online graph filtering algorithms in terms of accuracy, demonstrating the potential of LLMs in effectively addressing partially observed signals in graphs.
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