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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2014 Vol. 4(5): 405-410 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V4.445

Predicting Cooperation Relationships in Heterogeneous Movie Networks

Wei-Chin Hung, Hung-Wei Lin, Yu-Chung Tsao, and Wei-Guang Teng
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: wgteng@mail.ncku.edu.tw).
Yu-Chung Tsao is with the Department of Industrial Management, National Taiwan University of Science and Technology.

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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.

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