Detecting early-warning signals for social emergencies by temporal network sociomarkers

The evolution and development of social systems are always accompanied by social emergencies, which affect people's ordinary lives and even life safety. This study attempts to solve the problem of social system modeling and emergency prediction. First, the complex social relations in the social system are abstracted through the hierarchical inference model on temporal networks. Then, based on the critical transition theory in the dynamical system, the temporal network sociomarkers (TNS) method is proposed to perceive the tipping point of social emergencies. Experiments on two social system datasets verify that the bifurcation phenomenon also exists in social systems. The TNS method captures early warning signals (EWSs) before social emergencies and the sensitive groups that may be responsible for them. To our best knowledge, this study constructs a bridge between critical transition theory and complex social systems, providing theoretical and methodological support in security management, crisis intervention, and even battlefield situations with broad application prospects and great promotion value.

Automated summary

This study aims to solve the problem of social system modeling and emergency prediction by using the hierarchical inference model and critical transition theory. Experiments on two social system datasets confirm the effectiveness of the TNS method in capturing early warning signals and identifying sensitive groups.