Abstract

Social network has become the main platform for people to obtain information and connect with each other. Matching user accounts can help us build better users’ profiles and benefit many applications. It has attracted much attention from both industry and academia. At present, cross-platform user identification can be divided into three categories: based on user basic attribute information, user online social structure relationship and user behavior. Research on mobile social networks is a kind of dynamic mixed data analysis. Due to the remarkable heterogeneity of its data across platforms and the incomplete and untrue information caused by users’ concealment behavior, the recognition rate of the algorithm is relatively low. The paper provides a new matching user accounts method based on location verification. First, the self-centered network algorithm is applied to find cross-network edges in the respective networks of the two users to be matched, which is taken as the initial similarity value of the two users. Secondly, the longitude, latitude and time coordinates of a single platform node were used to modify the similarity. Specific 5 time points were selected within 24 hours and the error range of 10min was taken as the calculation method of great circle distance. Thirdly, since the user did not log in a certain social platform in a certain period of time, the convolutional neural network algorithm was adopted to mark the trajectory. Finally, all users in the whole network are identified by iterative operation. Experimental results on artificial random networks and real social networks show that the proposed algorithm has a high readiness rate and recall rate.