Resumen
Recently, spatial interaction analysis of online social networks has become a big concern. Early studies of geographical characteristics analysis and community detection in online social networks have shown that nodes within the same community might gather together geographically. However, the method of community detection is based on the idea that there are more links within the community than that connect nodes in different communities, and there is no analysis to explain the phenomenon. The statistical models for network analysis usually investigate the characteristics of a network based on the probability theory. This paper analyzes a series of statistical models and selects the MDND model to classify links and nodes in social networks. The model can achieve the same performance as the community detection algorithm when analyzing the structure in the online social network. The construction assumption of the model explains the reasons for the geographically aggregating of nodes in the same community to a degree. The research provides new ideas and methods for nodes classification and geographic characteristics analysis of online social networks and mobile communication networks and makes up for the shortcomings of community detection methods that do not explain the principle of network generation. A natural progression of this work is to geographically analyze the characteristics of social networks and provide assistance for advertising delivery and Internet management.