Abstract—In social network analysis, relationship prediction
among people in the interpersonal network is a broadly
discussed problem. Nevertheless, when modeling a real network
as a heterogeneous information network instead of a
homogeneous one, this problem becomes more challenging. In
this work, we focus on the movie network constituted by
multiple types of entities (e.g., movies, participants, studios, and
genres) and multiple types of links among these entities. To
clearly represent the semantic meanings in such a movie
network, we utilize the meta-path-based prediction model.
Advantages of our approach are two-fold. First, the
meta-path-based method systematically retrieves topological
features in a movie network. Second, we use the supervised
method to learn the best weights connected with different
topological features in building cooperation relationships.
Empirical studies based on the real IMDb dataset show that our
approach precisely predicts cooperation relationships in a
large-scale movie network.
Index Terms—Social network analysis, link prediction, heterogeneous information network.
Wei-Chin Hung, Hung-Wei Lin, and Wei-Guang Teng are with the Department of Engineering Science, National Cheng Kung University, Tainan, Taiwan (e-mail: firstname.lastname@example.org).
Yu-Chung Tsao is with the Department of Industrial Management, National Taiwan University of Science and Technology.
Cite: Wei-Chin Hung, Hung-Wei Lin, Yu-Chung Tsao, and Wei-Guang Teng, "Predicting Cooperation Relationships in Heterogeneous Movie Networks," International Journal of Machine Learning and Computing vol. 4, no. 5, pp. 405-410, 2014.