A Community Detection Approach for Smart-Phone Addiction Recognition

In this paper, we present a novel approach for Smart-Phone Addiction recognition that leverages community detection algorithms from the Social Network Analysis (SNA) theory. Our basic idea is to model data concerning users’ behavior while they are using mobile devices as a particular social graph, discovering by means of SNA facilities patterns that better identify users with a high predisposition to smart phone addiction. Eventually, several experiments on a sample of users monitored for several weeks have been carried out to verify effectiveness of the proposed approach in correctly recognizing the related addiction degree.