Mobile social applications are popular as the proliferating of mobile devices. Understanding user social behaviors is important to improve mobile social applications and enhance its quality of service. However, there is still lack of data for real deployment mobile social application on data analysis of human interaction and social behaviors in mobile social networks.   In this paper, we introduce the experiment methodology of deploying the Goose software in two campuses located in Germany and China respectively. Goose is a mobile social network application allows microblogging, message sending. With the help of volunteers, we collect user interaction data in the duration of 15 days. Based on the collected data, our observation reveals the following aspects of user interactions and their influences. First, user overall activities approximately match user daily life work pattern with a slightly longer time duration and periodically appearance. Second, user encounters in mobile social network follow the heavy tail distribution in small social communities, and user interactions follow the Pareto principle, where about 20% of users make close connections to the other users. Third, communication path between a pair of mobile nodes is mostly within 6 hops, and information diffusion using an epidemic strategy demonstrates that the informed population reaches to 50% in a short term and approaches to 80% in a long term.
['Konglin Zhu', 'Pan Hui', 'Yang Chen', 'Xiaoming Fu', 'Wenzhong Li']
Exploring user social behaviors in mobile social applications