A new model for nickname detection based on network structure and similarity propagation

Users can participate in variety topics and express their opinions using different kinds of online applications, and the IDs (nickname) they used are usually virtual and difficult for finding the physical person. Which pose great challenges for network security management and user's online behavior supervision. Focus on this problem, we proposed methods for nickname detection based on the user's online connection structure and similarity propagation model. Firstly, we collected user's profile information from two popular online applications, sina microblog and RenRen network. Then we mark several matched pairs which belong to the same person from the applications with hardly manually effort, and those IDs are selected as seed set for nickname detection. Secondly, we proposed an nickname detection model based on the connection structure and similarity propagation. We selected one matched pairs from the seed set and obtain all their neighbors. Then we calculated the similarity of each pairs from the neighbor set and calculate their neighbors' connection similarity and neighbors' location similarity. If the similarity is bigger than a selected threshold, we claim they are matched pairs and insert them into the seed sets. On one hand, the correlation results can propagated based on the updated seed set. On the other hand, the computational complexity are greatly reduced as we only employ the neighbors' profiles to calculate the similarity. Experimental results verify the efficiency of the proposed method, which can lay a solid foundation for the online network management and user's behavior supervision.