Zhenguo Xu received a Bachelor's degree in Information Systems from Shenyang Aerospace University and a Master's degree in Computational Intelligence from Cranfield University. Systematically studied in the field of computer science and during his master's studies at Cranfield University he focused on computational intelligence for data analysis.
After completing his Master's degree at Cranfield University, Zhenguo chose to continue his PhD at Cranfield University with a research topic on 'Temporal and Spatial Pattern Analysis Using Complex Network and Machine Learning'.
Currently research areas are focused on the following research directions:
- Processing data with the perspective of a complex network
Nowadays a lot of data is presented in the form of networks, e.g. traffic networks, computer networks, knowledge graphs, etc. But in many cases the relationships within the data are often complex and in some cases the structure of the data often affects the function of the data, e.g. the molecular structure of organisms and the molecular structure of drugs. It is therefore important to look at these complex data in the perspective of a complex system rather than simply as a graph.
- Using machine learning to analyse complex network
When faced with large amounts of data from complex networks, the proper use of tools to analyse them efficiently is an important topic. Zhenguo is intended to use machine learning algorithms to perform specific analyses on certain types of data, such as link prediction, node classification, community detection, etc.
- Applying graph neural networks(GNN) to the analysis of complex network
It is undeniable that machine learning is now rapidly being replaced by deep learning because of the efficiency and accuracy it demonstrates in analysing data or extracting features. For complex networks, GNN may be excellent for extracting and analysing features from complex networks due to their similar structure.