Temporal Graph Neural Network
Temporal Graph Neural Networks (TGNNs) aim to analyze and predict the evolution of relationships within dynamic networks by incorporating both the structure of the graph and the temporal dynamics of its interactions. Current research emphasizes improving the efficiency and scalability of TGNNs, often through novel architectures like Transformers and optimized training strategies, as well as enhancing interpretability and addressing challenges like data sparsity and noise. This field is significant for its applications in diverse areas such as traffic forecasting, recommendation systems, and crime prediction, offering more accurate and insightful models for complex real-world phenomena.
Papers
ST-MLP: A Cascaded Spatio-Temporal Linear Framework with Channel-Independence Strategy for Traffic Forecasting
Zepu Wang, Yuqi Nie, Peng Sun, Nam H. Nguyen, John Mulvey, H. Vincent Poor
Interaction-Aware Personalized Vehicle Trajectory Prediction Using Temporal Graph Neural Networks
Amr Abdelraouf, Rohit Gupta, Kyungtae Han
Time-varying Signals Recovery via Graph Neural Networks
Jhon A. Castro-Correa, Jhony H. Giraldo, Anindya Mondal, Mohsen Badiey, Thierry Bouwmans, Fragkiskos D. Malliaros
BUAA_BIGSCity: Spatial-Temporal Graph Neural Network for Wind Power Forecasting in Baidu KDD CUP 2022
Jiawei Jiang, Chengkai Han, Jingyuan Wang