  People usually get involved in multiple social networks to enjoy new services
or to fulfill their needs. Many new social networks try to attract users of
other existing networks to increase the number of their users. Once a user
(called source user) of a social network (called source network) joins a new
social network (called target network), a new inter-network link (called anchor
link) is formed between the source and target networks. In this paper, we
concentrated on predicting the formation of such anchor links between
heterogeneous social networks. Unlike conventional link prediction problems in
which the formation of a link between two existing users within a single
network is predicted, in anchor link prediction, the target user is missing and
will be added to the target network once the anchor link is created. To solve
this problem, we use meta-paths as a powerful tool for utilizing heterogeneous
information in both the source and target networks. To this end, we propose an
effective general meta-path-based approach called Connector and Recursive
Meta-Paths (CRMP). By using those two different categories of meta-paths, we
model different aspects of social factors that may affect a source user to join
the target network, resulting in the formation of a new anchor link. Extensive
experiments on real-world heterogeneous social networks demonstrate the
effectiveness of the proposed method against the recent methods.
