1 citations · 1 across the 6 of their papers we have counts for
7 papers
Signal Processing over Time-Varying Graphs: A Systematic Review
Yi Yan, Jiacheng Hou, Zhenjie Song +1
As irregularly structured data representations, graphs have received a large amount of attention in recent years and have been widely applied to various real-world scenarios such a…
LLM Online Spatial-temporal Signal Reconstruction Under Noise
Yi Yan, Dayu Qin, Ercan Engin Kuruoglu
This work introduces the LLM Online Spatial-temporal Reconstruction (LLM-OSR) framework, which integrates Graph Signal Processing (GSP) and Large Language Models (LLMs) for online…
Graph Signal Adaptive Message Passing
Yi Yan, Changran Peng, Ercan Engin Kuruoglu
This paper proposes Graph Signal Adaptive Message Passing (GSAMP), a novel message passing method that simultaneously conducts online prediction, missing data imputation, and noise…
Time-varying Graph Signal Estimation via Dynamic Multi-hop Topologies
Yi Yan, Fengfan Zhao, Ercan Engin Kuruoglu
The assumption of using a static graph to represent multivariate time-varying signals oversimplifies the complexity of modeling their interactions over time. We propose a Dynamic M…
LLM-based Online Prediction of Time-varying Graph Signals
Dayu Qin, Yi Yan, Ercan Engin Kuruoglu
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 tempor…
Adaptive Least Mean Squares Graph Neural Networks and Online Graph Signal Estimation
Yi Yan, Changran Peng, Ercan Engin Kuruoglu
The online prediction of multivariate signals, existing simultaneously in space and time, from noisy partial observations is a fundamental task in numerous applications. We propose…
Adaptive Spatio-temporal Estimation on the Graph Edges via Line Graph Transformation
Yi Yan, Ercan Engin Kuruoglu
Spatial-temporal estimation of signals on graph edges is challenging because most conventional Graph Signal Processing techniques are defined on the graph nodes. Leveraging the Lin…