KaTaGCN: Knowledge-Augmented and Time-Aware Graph Convolutional Network for efficient traffic forecasting

Yuyan Wang, Jie Hu, Fei Teng, Lilan Peng, Shengdong Du, Tianrui Li

Abstract: Highlights•Propose a loss function that utilizes prior knowledge to guide adaptive graph learning.•Design a dynamic time-aware network to capture the time patterns on each node.•A concise and efficient framework without any attention mechanism or RNN structure.