A single-directional influence topic model using call and proximity logs simultaneously

Abstract

Understanding social interactions is one of the key factors in the development of context-aware ubiquitous applications. Identifying interaction patterns sensed by a mobile device is one possible way for understanding social interactions. Most previous studies on this problem have employed call and proximity logs to represent social interactions. Because these interactions can be characterized by topics, the studies have applied topic models based on latent Dirichlet allocation (LDA) to identifying interaction patterns from social interactions. However, these previous studies regarded calls and proximities as independent interaction types. As a result, they lost the information obtainable when calls and proximities were analyzed simultaneously. This paper proposes a topic-based method that simultaneously considers calls and proximities, allowing interaction patterns to be identified 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. From the observation that the number of proximities in a mobile log usually overwhelms that of calls and the proximities are observed regularly, the proposed method models a single-directional influence from proximities to calls, where both call and proximity are modeled by LDA. The experiments with three different data sets from the Massachusetts Institute of Technology’s Reality Mining project show that the proposed method outperforms the method that considers calls and proximities independently; this proves the plausibility of the proposed method.