CDAGF
C. Ji, Y. Xu, Y. Lu, X. Huang, Y. Zhu, "Contrastive-Learning-Based Adaptive Graph Fusion Convolution Network with Residual-Enhanced Decomposition Strategy for Traffic Flow Forecasting", IEEE Internet of Things Journal, Volume 11, pages 20246-20259, 2024.
SCPT
A. Prabowo, H. Xue, W. Shao, P. Koniusz, F. Salim, "Traffic Forecasting on New Roads using Spatial Contrastive Pre-Training (SCPT)", Data Mining and Knowledge Discovery, Volume 38, pages 913-937, 2024.
STS-CCL
L. Li, K. Yang, J. Bi, F. Luo, "STS-CCL: Spatial-Temporal Synchronous Contextual Contrastive Learning for Urban Traffic Forecasting", EEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2024, pages 6705-6709, 2024.
COGCN
K. Guo, D. Tian, Y. Hu, Y. Sun, Z. Qian, J. Zhou, J. Gao, B. Yin, "Contrastive Optimized Graph Convolution Network For Traffic Forecasting", Neurocomputing, Volume 602, October 2024.
CPT
M. Low, A. Prabowo, H. Xue, F. Salim, “Embedding Spatial Context in Urban Traffic Forecasting with Contrastive Pre-Training”, Preprint, March 2025.
ST-CML
H. Lyu, C. Wang, “ST-CML: A Contrastive Meta Learning Framework for Spatio-Temporal Graph Few-Shot Learning with Cross-City Transfer”, Preprint, May 2025.