APA (7th ed.) Citation

Zhu, X., Huang, L., Zhou, G., Yang, J., & Duan, C. (2026). PC-LossGNN: A Physics-Consistent Spatiotemporal Graph Neural Network for Line Loss Anomaly Classification. Symmetry (20738994), 18(6), 1052. https://doi.org/10.3390/sym18061052

Chicago Style (17th ed.) Citation

Zhu, Xiaojing, Li Huang, Gan Zhou, Junyang Yang, and Chengge Duan. "PC-LossGNN: A Physics-Consistent Spatiotemporal Graph Neural Network for Line Loss Anomaly Classification." Symmetry (20738994) 18, no. 6 (2026): 1052. https://doi.org/10.3390/sym18061052.

MLA (9th ed.) Citation

Zhu, Xiaojing, et al. "PC-LossGNN: A Physics-Consistent Spatiotemporal Graph Neural Network for Line Loss Anomaly Classification." Symmetry (20738994), vol. 18, no. 6, 2026, p. 1052, https://doi.org/10.3390/sym18061052.