Integrating Attribute Information into Relational Data: A Latent Structure Approach

Various analytical methods for diverse data have been proposed with the evolution of information technology. This study focuses on relational data, comprising information on relationships between objects. Although several studies have focused on the relationships between objects, only a few have utilized attribute information. The conventional study proposed analyzing relational data with attribute information using a latent structure model. However, the model assumes that both attribute information and relational data are generated by a single latent variable. Furthermore the model does not effectively handle missing values in relational data. Therefore, in this study, we modify the conventional latent structure analysis model for relational data with attribute information, which assumes that latent variables have various types of structures, and derived a method for estimating its parameters. In addition, we apply the proposed model to cases when the relational data contain missing values. The effectiveness of our proposed method is demonstrated through simulation experiments on synthetic dataset.