Analyzing User Relationships in Weibo Networks: A Bayesian Network Approach

In recent years, online social networks such as Facebook, Twitter and Sina Weibo are more and more popular and there is a highly increasing interest of studying the relationships among the large amount of microblogging users. In this paper, the link prediction method is utilized to analyze the relationships between Sina Weibo users. Firstly, the topological features of Weibo network are studied and the influence of topological structure features to the formation of Sina Weibo network is verified. Then the attribute features of Sina Weibo are also considered and analyzed. In this paper, a link prediction model is introduced based on Bayesian networks classifier combining the two types of features together. The experiments are conducted with the datasets crawled from Sina Weibo site. We compare the experiment results with and without the attribute features and rank the importance of features, finding out that the attribute features have a significant effect on the formation of Weibo users' relationships besides the topological structure features, and contribute significantly to the improvement of the predictive performance.

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