Adaptive Spatial-Temporal Graph Convolution Networks for Collaborative Local-Global Learning in Traffic Prediction
Author:
Affiliation:
1. College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
2. School of Computer Science and Engineering, Nanyang Technological University, Singapore
Funder
Key Program of the National Natural Science Foundation of China
China Scholarship Council
Postgraduate Scientific Research Innovation Project of Hunan Province
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Aerospace Engineering,Automotive Engineering
Link
http://xplorestaging.ieee.org/ielx7/25/10287112/10125071.pdf?arnumber=10125071
Reference43 articles.
1. Semi-supervised classification with graph convolutional networks;kipf;Proc Int Conf Learn Representations,0
2. Spatiotemporal Attention-Based Graph Convolution Network for Segment-Level Traffic Prediction
3. Stam: A spatiotemporal attention based memory for video prediction;chang;IEEE Trans Multimedia,2022
4. FTPG: A Fine-Grained Traffic Prediction Method With Graph Attention Network Using Big Trace Data
5. Approximate personalized propagation for unsupervised embedding in heterogeneous graphs
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