# LLM-based Online Prediction of Time-varying Graph Signals

arXiv: https://arxiv.org/abs/2410.18718 · HTML: https://mlsanity.com/paper/2410.18718/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 9 references that are in the map.
- First posted on arXiv 24 Oct 2024.

## Builds on (most cited first, 2 of 2)

- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Adaptive Least Mean Squares Estimation of Graph Signals](https://mlsanity.com/paper/1602.05703/) (2016)

## Built on by (most cited first, 3 of 3)

- [AquaSentinel: Next-Generation AI System Integrating Sensor Networks for Urban Underground Water Pipeline Anomaly Detection via Collaborative MoE-LLM Agent Architecture](https://mlsanity.com/paper/2511.15870/) (2025)
- [ParaFormer: A Generalized PageRank Graph Transformer for Graph Representation Learning](https://mlsanity.com/paper/2512.14619/) (2025)
- [LLMersion: A Local-First AI Agent Framework for Low-Cost Home Language Learning toward Educational Equity](https://mlsanity.com/paper/2609.29672/) (2026)

## Abstract (arXiv)

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.