A Community Detection Approach for Smart-Phone Addiction Recognition

Fabio Cozzolino, Vincenzo Moscato, Antonio Picariello, Giancarlo Sperli

Abstract

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.

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Paper Citation


in Harvard Style

Cozzolino F., Moscato V., Picariello A. and Sperli G. (2019). A Community Detection Approach for Smart-Phone Addiction Recognition.In Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-377-3, pages 53-64. DOI: 10.5220/0007839100530064


in Bibtex Style

@conference{data19,
author={Fabio Cozzolino and Vincenzo Moscato and Antonio Picariello and Giancarlo Sperli},
title={A Community Detection Approach for Smart-Phone Addiction Recognition},
booktitle={Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2019},
pages={53-64},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007839100530064},
isbn={978-989-758-377-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - A Community Detection Approach for Smart-Phone Addiction Recognition
SN - 978-989-758-377-3
AU - Cozzolino F.
AU - Moscato V.
AU - Picariello A.
AU - Sperli G.
PY - 2019
SP - 53
EP - 64
DO - 10.5220/0007839100530064