The latest advance in recommendation shows that better user and item representations can be learned via performing graph convolutions on the user-item interaction graph. However, such finding is ...

In this article, we propose a novel dynamic graph convolution and spatiotemporal self-attention (DGSTA) network for traffic flow prediction. Specifically, considering the large amount of short-term ...

An Adaptive Diffusion Graph Convolutional Network (ADGCN) is then employed to model both local and global spatial correlations. In addition, we design a cross-gated spatio-temporal fusion mechanism ...

Zeng et al. (2020) proposed the hierarchical graph convolution (HGCN) network for classification tasks using topological relationships between each electrode, where power spectral density and ...

To summarize, the main contributions of this study are as follows: 1. This study proposes a novel children ASD evaluation approach via joint analysis of EEG and eye-tracking recordings using graph ...

Modeling freshwater plankton community dynamics with static and dynamic interactions using graph convolution embedded long short-term memory. Journal: Water Research Published: 2024-09-06 DOI: ...