Mountain Top Algorithm: Complex Historical-Geographic Network Data Analysis based on Structure, Dynamics, and Function

This paper constructs a social knowledge/genetic algorithm model based on a mathematical approach that always relates the behavior time, behavior distance, and geo-data acquisition quality of all mobility patterns to the overall geographic knowledge evolution process. In order to address the existence of multiple interactions in complex data systems in the social computing domain, such as transportation networks (mountain, geography, and accessibility) under different geo-constraints, including interaction and survival needs in socio-ecological networks, including agro-genetic, demographic, and economic interaction networks, we realize how to find the peaks in the data network, i.e., the few “nuclei” with high connectivity, in the data organization process. The model can be used to identify the nuclei of high connectivity in the data network, and the key role of the nuclei in the effective derivation of social computing, and to algorithmically describe the above behavior. The model can be used both to discover the causes of in situ behavior at the macro level and to provide new solutions for modeling coupled object-event-process data in complex social computing.