| Summary: | 碩士 === 國立成功大學 === 工程科學系 === 102 === 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.
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