This article lists some key ideas of the MTGNN paper.

Key Components

Time Convolution (TC) Module

Time Convolution
The temporal convolution is responsible for capturing temporal patterns in a sequence.

Graph Convolution Module

Mix-hop Propagation in GNN
Mix-hop is a strategy to avoid oversmoothing in GNN

Graph Structure Learning Layer

Graph Structure Learning in GNN
We can learn a graph structure without prior knowledge


Wu et al., 2020

Wu et al., 2020

Planted: by ;

L Ma (2022). 'MTGNN', Datumorphism, 11 April. Available at: