We propose a novel structured federated learning (SFL) framework to learn both the global and personalized models simultaneously using client-wise relation graphs and clients' private data.
In such a graph, the correlations between different nodes at different time steps are not explicitly reflected, which may restrict the learning ability of graph neural networks.
Representation learning on temporal interaction graphs (TIG) is to model complex networks with the dynamic evolution of interactions arising in a broad spectrum of problems.
This paper aims to unify spatial dependency and temporal dependency in a non-Euclidean space while capturing the inner spatial-temporal dependencies for traffic data.
Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic.
Ranked #2 on Univariate Time Series Forecasting on Electricity
Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system.
Ranked #8 on Traffic Prediction on PeMS07
In this survey, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields.