  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.
