Data-driven Modeling and Optimization: Applications to Social Computing

Due to the emergence of the Internet and mobile web, granular data related to various aspects of human behavior such as human mobility, interpersonal contacts, and telecommuting can be accumulated much easier than before. The advancement of artificial intelligence and machine learning techniques also dramatically increases the potential of data-driven studies to improve our society.

This Research Topic aims to explore different areas and collect articles that focus on data-driven social computation. Furthermore, we hope to get more insights regarding the applications of such approaches in various areas such as public health, social physics, intelligent transportation system, network security, economics, and medical affairs.

High-quality Original Research, Review and Perspective articles are welcome to submit to this Research Topic. Areas of interests include but are not limited to the following themes:

1. Analysis of complex systems from the perspective of network science
2. Data-driven epidemic propagation or social network investigation
3. Data-driven vaccination aiming to study epidemic propagation
4. Artificial attack and defense of various network
5. Transportation predication through artificial intelligence approaches
6. Medical resource allocation through artificial intelligence approaches
7. Data-driven financial market investigation or other aspects related to social physics or smart city
8. Intelligent software system application to social computing