Finding social interaction patterns using call and proximity logs simultaneously

Yong-Jin Han, Shao Bo Cheng, Se-Young Park, Seong-Bae Park

Abstract: 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.