Identifying users across social networks based on dynamic core interests

With the development of social networks, most of users hold serval accounts in different social network platforms. It is a very important task to match users' varying identities in the internet. Plenty of existing approaches attempt to link users via comparing social structures, mapping users' profiles and analyzing users' authority. Those existing approaches fail to consider the dynamic changes of users. In the paper, we introduce human behavioral limitations in social networks. And then based on the limitations, we propose a dynamic core interests mapping (DCIM) algorithm, which jointly consider the users' social network structures and users' article content to identify users over platforms. The algorithm firstly models user's core interests and then calculates the similarity of two target users using DCIM. Our experiments use real world datasets from Twitter and BlogCatalog. The results of experiments show that our method is effective on mapping users across social networks. And the algorithm is significantly more effective than baseline methods such as FNN and MAG.