117 citations · 194 across the 4 of their papers we have counts for
5 papers · 1 filter
When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series
Chen Shao, Yue Wang, Zhenyi Zhu +4
Modeling multivariate time series by representing them as graphs, where individual series act as nodes and pairwise temporal corre- lations serve as edges, has gained significant t…
Personalized Federated Learning With Graph
Fengwen Chen, Guodong Long, Zonghan Wu +2
Knowledge sharing and model personalization are two key components in the conceptual framework of personalized federated learning (PFL). Existing PFL methods focus on proposing new…
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
Zonghan Wu, Shirui Pan, Guodong Long +3
Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic. A basic assumptio…
Graph WaveNet for Deep Spatial-Temporal Graph Modeling
Zonghan Wu, Shirui Pan, Guodong Long +2
Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system. Existing approaches mostly capture the spatial…
A Comprehensive Survey on Graph Neural Networks
Zonghan Wu, Shirui Pan, Fengwen Chen +3
Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language unde…