Abstract
With the growth of social services, more and more social networks are owned by the same company. An important problem, called the target node prediction problem, to the owner of multiple social networks is to identify those users from one network (called the source network) who are likely to join another (called the target network) to become the target users, so that the advertisements can be placed more precisely and economically. Although this problem can be solved using existing techniques in the field of cross domain learning, we observe that in many real-world situations the cross-domain classifiers perform sub-optimally due to the following reasons. First, most of the existing works do not take into account the contents of edges, which are common in practice and encode valuable information. Second, since the target and source networks may be formed by different reasons and evolve distinctively, they may be heterogeneous, preventing the existing cross domain classifiers from transferring the knowledge from the target network to source network. In this paper, we propose the Consistent Incidence Co-Factorization (CICF) that helps the knowledge transfer between social networks. The CICF uses the edge-specific information to find better latent factors, and copes with the heterogeneity by transferring knowledge only through those common users that behave consistently in the two networks. Extensive simulations are conducted and the results demonstrate the effectiveness of CICF, either when it is used directly to predict the target users or indirectly as a preprocessing tool for existing classifiers.