A correlation method of online user identity attributes based on a propagation model of username similarities

A user identity attribute correlation method is proposed to focus on the problem that behaviors of online users are hard for fusion and supervision among multi-online applications due to their complexity and variation. The method is based on a propagation model of username similarities. The English and Chinese characters in usernames are separated by considering the characteristics of username in the Chinese social networks. A greedy algorithm is used to extract the longest common sequence between English and Chinese characters respectively for different usernames, and then username similarities are calculated. User's online connection structure and the username similarities of their first-order neighbors are used to decide the matching degree of the selected user pairs. Hence, not only the username similarity is propagated quickly among the connection networks, but also the complexity of matching calculation is greatly reduced. Experimental results based on the datasets collected from Sina Microblog and Renren networks show that the proposed algorithm improves the matching accuracy of usernames by about 30%, and the propagation model greatly reduces the calculation complexity of username similarities. The analysis results achieve the goal of user's behavior fusion among different online applications, and have a reference value for online network security management and user's online behavior supervision.