Many AI algorithms, especially graph learning methods, have been proposed to analyze brain networks. An important issue for existing graph learning methods is that those models are not typically easy ...

Furthermore, the graph regression was synchronously performed in three latent spaces, including association space, miRNA similarity space, and disease similarity space, by using two matrix ...

In this article, we propose a new linear regression (LR)-based multiclass classification method, called discriminative regression with adaptive graph diffusion (DRAGD). Different from existing graph ...

We study a graph-constrained regularization procedure and its theoretical properties for regression analysis to take into account the neighborhood information of the variables measured on a graph.

We propose a novel unified spatial–temporal regression framework named Generalized Spatial–Temporal Regression Graph Convolutional Transformer (GSTRGCT) that extends panel model in spatial ...

The complexity of tuning and intensive computation required by deep models often leads to overfitting when applied to small data sets. When dealing with small data sets, traditional machine learning ...

We experiment on 2 benchmarks for traffic flow forecasting. We compare our model with fourteen baselines, including traditional spatial-temporal regression models , such as ME, FE, and RE, and deep ...