2013/11/12 by Yutao Zhang, Jie Tang, Zhang, Yutao +1
Computer Science · Physics and Astronomy · #FOS: Computer and information sciences #FOS: Physical sciences #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #cs.SI #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.1311.2670
arxiv created 2014/01/21 · arxiv updated 2014/01/22
In this work, we formulate the problem of social network integration. It takes multiple observed social networks as input and returns an integrated global social graph where each node corresponds to a real person. The key challenge for social network integration is to discover the correspondences or interlinks across different social networks. We engaged an in-depth analysis across three online social networks, AMiner, Linkedin, and Videolectures in order to address what reveals users' social identity, whether the social factors consistent across different social networks and how we can leverage these information to perform integration. We proposed a unified framework for the social network integration task. It crawls data from multiple social networks and further discovers accounts correspond to the same real person from the obtained networks. We use a probabilistic model to determine such correspondence, it incorporates features like the consistency of social status and social ties across different, as well as one-to-one mapping constraint and logical transitivity to jointly make the prediction. Empirical experiments verify the effectiveness of our method.