… forecasting method called the temporal graph convolutional network (T-… temporal
dependences from traffic data at the same time, we propose a temporal graph convolutional network …

… In this paper, a novel attention based spatial-temporal graph convolution model called …
spatial-temporal attention mechanism and the spatial-temporal convolution, including graph …

… Spatial temporal graph convolution. First, we evaluate the necessity of using spatial temporal
graph convolution operation. We use a baseline network architecture (Kim and Reiter 2017…

… temporal graph convolutional network (A3T-GCN) was proposed to simultaneously capture
global temporal … of the road network through the graph convolutional network. Moreover, the …

… spatial-temporal graph convolutional module to synchronously capture the localized
spatialtemporal correlations directly, instead of using different types of neural network modules …

… and temporal dependencies. In this paper, we propose a novel deep learning framework,
Spatio-Temporal Graph Convolutional Networks (… Instead of applying regular convolutional and …

… Temporal Multi-Graph Convolutional Network), a unified deep learning framework, which
integrates spatial, temporal… for traffic flow prediction in a road network. The contributions of this …

… relation modeling of graph convolutional network (GCN) [… graph representation of the video,
which be denoted as Gt(Vt, Et). Then, we perform graph convolution operation on the graph…

… the embeddings and learn the temporal dynamics. These methods … , which adapts the graph
convolutional network (GCN) model … Spatio-temporal graph convolutional networks: A deep …

… To address this challenge, we propose the temporal graph convolutional network (TGCN),
a model that leverages temporal and structural information and has relatively few parameters. …