I am currently pursuing a master’s degree in the School of Computer Science at Sichuan Normal University, Chengdu, China. Under the supervision of Prof. Junli Li.

My research interests include Complex Networks, Graph Machine Learning, and Graph Representation Learning. Recently, my research work mainly focuses on using deep learning approaches (graph neural networks, convolutional neural networks) for efficient and accurate robustness approximation of complex networks. Additionally, I am exploring the integration of graph machine learning and complex network knowledge, as well as the application of graph neural networks in complex network research. I am also interested in exploring the integration of Large Language Models (LLMs) with graph learning techniques and associated graph-based tasks.

News*

• [03/2024] Paper accepted by International Joint Conference on Neural Networks.
• [10/2023] I have been awarded the National Scholarship.
• [07/2023] Paper accepted by IEEE Transactions on Circuits and Systems I: Regular Papers.
• [06/2023] Paper accepted by IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2023.
• [04/2023] Paper accepted by IEEE Transactions on Neural Networks and Learning Systems.

Selected Publications

1. Chengpei Wu, Yang Lou, and Junli Li “Pyramid Pooling-Based Local Profiles for Graph Classification” IEEE International Conference on Systems, Man, and Cybernetics (SMC) October 1-4, 2023, Maui, Hawaii, USA.
2. Chengpei Wu, Yang Lou, Lin Wang, Junli Li, and Guanrong Chen, “SPP-CNN: An Efficient Framework for Network Robustness Prediction,” IEEE Transactions on Circuits and Systems I: Regular, doi:10.1109/TCSI.2023.3296602
3. Chengpei Wu, Yang Lou, Ruizi Wu, Wenwen Liu, and Junli Li “CNN-based Prediction of Network Robustness With Missing Edges” In Proceedings of International Joint Conference on Neural Networks (IJCNN) Padua, Italy, doi:10.1109/IJCNN55064.2022.9892188
4. Yang Lou, Chengpei Wu, Junli Li, Lin Wang, and Guanrong Chen , “Network Robustness Prediction: Influence of Training Data Distributions ,” IEEE Transactions on Neural Networks and Learning Systems, doi:10.1109/TNNLS.2023.3269753