With the increasing demand for sensing, processing, and storing capabilities of modern sensors, massive volumes of information-rich data are now rapidly expanding in many physical and social domains, ranging from robotics to biological and economic sciences.Data are often dynamically generated by complex interconnected processes, and encode key information about the structure and operation of these networked phenomena. Therefore, the complex networks have become a system with many factors, and the modeling and optimization designed by data are generally applied to large-scale social computing with higher accuracy.

Therefore, we initiate this Special Issue on the recent developments, challenges, and opportunities of data-driven modeling and optimization for complex networks and its applications in social computing. We welcome original research and review articles on these topics.

Potential topics include but are not limited to the following:

• Data-driven financial market investigation or other aspects related to social physics or smart city
• Intelligent software system application to social computing
• Analysis of complex systems from the perspective of network science
• Data-driven intelligent modeling for social computing
• Stability and qualitative analysis of complex networks
• Modelling, analysis, simulation, and design of data-driven controls
• Existence of prime period and periodic points
• New data-driven models for dynamic analysis of complex networks