Welcome to Network Science and Graph Learning (NSGL) Lab
欢迎访问 网络科学与图学习实验室 主页
[实验室简介]
网络科学与图学习实验室 主要研究方向包括网络科学、图学习与优化, 同时也将相关方法应用于通信网络、生物网络、社交网络等复杂系统中的关键问题.
具体研究课题包括:
- 网络功能与结构鲁棒性的分析、预测与优化;
- 图学习与图神经网络的理论研究及应用;
- 复杂网络中关键节点识别与拓扑结构优化;
- 观点动力学建模与博弈分析;
- 图表示学习与图结构分类等问题.
我们实验室在广岛大学招收博士和硕士研究生 (奖学金信息);
同时, 在四川师范大学计算机学院招收硕士研究生 (与李均利教授共同指导).
实验室负责人楼洋现为日本广岛大学副教授、 四川师范大学校外硕士导师、IEEE高级会员、高等教育学院会士 (Fellow of the Higher Education Academy), 近年在 IEEE CIM, CSM, TCYB, TNNLS, TNSE, TCAS-I/II, RESEARCH 等期刊, 以及 ICLR, IJCNN, GECCO, SMC 等会议发表论文 50 余篇, 主持包括 JSPS, NSFC, NSTC 等多项国家级和省部级科研项目.
欢迎对网络科学、图学习、优化算法及应用 等研究课题感兴趣的同学加入我们实验室.
欢迎通过电子邮件 ylou[at]hiroshima-u[dot]ac[dot]jp 询问与交流.
[团队成员]
- Chengpei Wu, Ph.D. Student (Next-Generation AI Fellow), Hiroshima University
- Chengxi Chu, Ph.D. student, Universiti Malaya
- 王栋, 四川师范大学2025级硕士研究生, 研究方向: 观点动力学
- 胡雨, 四川师范大学2025级硕士研究生, 研究方向: 复杂网络与图机器学习
- 吴毅, 四川师范大学2025级硕士研究生, 研究方向: 复杂网络与图机器学习
- 阳昊, 四川师范大学2025级硕士研究生, 研究方向: 复杂网络与图机器学习
硕士毕业生:
- 吴成沛, 2024年毕业, 获国家奖学金、省级优秀毕业生, 现于广岛大学攻读博士学位.
- 武瑞梓, 2022年毕业, 获国家奖学金、省级优秀毕业生.
- 陈亮, 2026年毕业, 主要成果： L. Chen, Y. Lou*, L. Wang, and G. Chen, "D2R: A Distance Metric for Exploring Network Structural Robustness Enhancement Potential," Reliability Engineering & System Safety, 270: 112173 (2026)
[实验室主要论文]
-
Y. Lou*,
C. Wu,
L. Chen, W. Huang, L. Zhou,
L. Wang,
and G. Chen,
"Exploring the Potential for Enhancing Structural Robustness of Complex Networks,"
IEEE Computational Intelligence Magazine,
20 (4): 83–98; doi:10.1109/MCI.2025.3599463 (2025) -
C. Wu,
Y. Lou*,
J. Li, L. Wang, S. Xie,
and G. Chen,
"A Multitask Network Robustness Analysis System Based on the Graph Isomorphism Network, "
IEEE Transactions on Cybernetics,
vol. 54, no. 11, pp. 6630–6642; doi:10.1109/TCYB.2024.3422430 (2024) [Open Access]
-
Y. Lou*,
C. Wu,
J. Li, L. Wang,
and G. Chen,
"Network Robustness Prediction: Influence of Training Data Distributions, "
IEEE Transactions on Neural Networks and Learning Systems,
vol. 35, no. 10, pp. 13496–13507; doi:10.1109/TNNLS.2023.3269753 (2024)
-
Y. Lou*, R. Wu, J. Li, L. Wang, X. Li,
and G. Chen,
"A Learning Convolutional Neural Network Approach for Network Robustness Prediction, "
IEEE Transactions on Cybernetics,
vol. 53, no. 7, pp. 4531–4544; doi:10.1109/TCYB.2022.3207878 (2023)
-
Y. Lou*, L. Wang,
and G. Chen,
"Structural Robustness of Complex Networks: A Survey of A Posteriori Measures, "
IEEE Circuits and Systems Magazine,
vol. 23, no. 1, pp. 12–35; doi:10.1109/MCAS.2023.3236659 (2023)
-
Y. Lou, Y. He, L. Wang,
and G. Chen,
"Predicting Network Controllability Robustness: A Convolutional Neural Network Approach, "
IEEE Transactions on Cybernetics,
vol. 52, no. 5, pp. 4052–4063; doi:10.1109/TCYB.2020.3013251 (2022) -
Y. Lou, Y. He, L. Wang, K.F. Tsang,
and G. Chen,
"Knowledge-Based Prediction of Network Controllability Robustness, "
IEEE Transactions on Neural Networks and Learning Systems,
vol. 33, no. 10, pp. 5739–5750; doi:10.1109/TNNLS.2021.3071367 (2022)
-
Y. Lou, R. Wu, J. Li, L. Wang,
and G. Chen,
"A Convolutional Neural Network Approach to Predicting Network Connectedness Robustness"
IEEE Transactions on Network Science and Engineering,
vol. 8, no. 4, 3209–3219; doi:10.1109/TNSE.2021.3107186 (2021)
-
Y. Lou, L. Wang, and G. Chen,
"Enhancing Controllability Robustness of q-Snapback Networks Through Re-directing Edges, "
Research,
vol. 2019, 7857534; doi:10.34133/2019/7857534 (2019)
[基础阅读]
- 复杂网络基础知识: Network Science by A.-L. Barabási, 2016 [Link]
- 图表示学习: Graph Representation Learning by W. Hamilton, 2020 [Link] [YouTube]
- 图神经网络: Graph Neural Networks by L. Wu, et al., 2023 [Link]
- 强化学习用于搜索关键节点: FINDER by C. Fan, et al., 2020 [Link] [Code]
- 复杂网络能控性鲁棒性: by Y. Lou, L. Wang, and G. Chen, 2026 [Link]
[实验室地址]
- 日本 广岛县 东广岛市 镜山一丁目3番2号 广岛大学 (东广岛校区)
- 中国 成都市 成龙大道二段1819号 四川师范大学 (成龙校区)
