Compact representation of graph data is a fundamental problem in pattern recognition and machine learning area. Recently, graph neural networks (GNNs) have been widely studied for graph-structured ...

Specifically, a hybrid graph convolution network is developed to effectively capture complex spatio-temporal dynamics. Meanwhile, a Transformer-based self-attention module helps ST-HAG to extract the ...