YANG LOU

Last Updated :2025/11/04

Affiliations, Positions
Graduate School of Advanced Science and Engineering, Associate Professor
Web Site
https://scholar.google.com/citations?user=MhN74UwAAAAJ&hl=en
https://fylou.github.io/
E-mail
ylouhiroshima-u.ac.jp
Self-introduction
Dr. Yang Lou received his Ph.D. degree from the Department of Electrical Engineering, City University of Hong Kong, in 2017. He is currently an Associate Professor at the Graduate School of Advanced Science and Engineering, Hiroshima University, Japan. From 2017 to 2025, he held research and academic appointments at City University of Hong Kong, Lingnan University, the University of Osaka, and National Yang Ming Chiao Tung University. His research interests include Network Science, Graph Learning, and Optimization. He has published over forty research papers, including dozens of papers in prestigious IEEE Magazines and Transactions such as CIM, CASM, TCYB, and TNNLS, as well as in renowned international conferences including ICLR, GECCO, and IJCNN. According to Google Scholar, his h-index is 19. Dr. Lou is a Senior Member of the IEEE and a Fellow of the Higher Education Academy (FHEA).

Basic Information

Major Professional Backgrounds

  • 2022/11, 2023/07, Osaka University, Researcher (Specially Appointed)
  • 2023/08, 2025/01, Natioal Yang Ming Chiao Tung University, Assistant Professor
  • 2025/02, 2025/07, National Yang Ming Chiao Tung University, Associate Professor
  • 2025/08/01, Hiroshima University, Graduate School of Advanced Science and Engineering, Associate Professor

Academic Degrees

  • City University of Hong Kong

Research Activities

Academic Papers

  1. ★, Exploring the Potential for Enhancing Structural Robustness of Complex Networks, IEEE Computational Intelligence Magazine, 20(4), 83-98, 202510
  2. ★, A Multitask Network Robustness Analysis System Based on the Graph Isomorphism Network, IEEE Transactions on Cybernetics, 54(11), 6630-6642, 202411
  3. ★, Network Robustness Prediction: Influence of Training Data Distributions, IEEE Transactions on Neural Networks and Learning Systems, 35(10), 13496-13507, 202410
  4. ★, A Learning Convolutional Neural Network Approach for Network Robustness Prediction, IEEE Transactions on Cybernetics, 53(7), 4531-4544, 202307
  5. ★, Structural Robustness of Complex Networks: A Survey of A Posteriori Measures, IEEE Circuits and Systems Magazine, 23(1), 12-35, 202301
  6. ★, Knowledge-Based Prediction of Network Controllability Robustness, IEEE Transactions on Neural Networks and Learning Systems, 33(10), 5739-5750, 202210
  7. ★, Predicting Network Controllability Robustness: A Convolutional Neural Network Approach, IEEE Transactions on Cybernetics, 52(5), 4052-4063, 202205
  8. Toward Stronger Robustness of Network Controllability: A Snapback Network Model, IEEE Transactions on Circuits and Systems I: Regular Papers, 65(9), 2983-2991, 201809
  9. A Comparative Study on Controllability Robustness of Complex Networks, IEEE Transactions on Circuits and Systems II: Express Briefs, 66(5), 828-832, 201905
  10. Towards Optimal Robustness of Network Controllability: An Empirical Necessary Condition, IEEE Transactions on Circuits and Systems I: Regular Papers, 67(9), 3163-3174, 202009
  11. Searching Better Rewiring Strategies and Objective Functions for Stronger Controllability Robustness, IEEE Transactions on Circuits and Systems II: Express Briefs, 68(6), 2112-2116, 202106
  12. A Convolutional Neural Network Approach to Predicting Network Connectedness Robustness, IEEE Transactions on Network Science and Engineering, 8(4), 3209-3219, 202110
  13. SPP-CNN: An Efficient Framework for Network Robustness Prediction, IEEE Transactions on Circuits and Systems I: Regular Papers, 70(10), 4067-4079, 202310