Finding social interaction patterns using call and proximity logs simultaneously

This paper proposes a topic-based method to reflect calls and proximities simultaneously into finding interaction patterns from a mobile log. For this purpose, the proposed method regards calls and proximities as a homogeneous information type that are drawn from the same temporal space expressed by the same distribution, but with different parameters. The number of proximities in a mobile log usually overwhelms that of calls and the proximities are observed regularly. Therefore, the proposed method models a single directional influence from proximities to calls, where both call and proximity are modeled by the Latent Dirichlet Allocation (LDA). According to the experiments on the data set from MIT´s Reality Mining project, the proposed method outperforms the method that treats calls and proximities independently, which proves the plausibility of the proposed method.