Reality Mining with Mobile Big Data: Understanding the Impact of Network Structure on Propagation Dynamics

TL;DR

This paper explores how mobile big data from IoT devices can be used to understand and model the influence of network structure on the spread of information and epidemics, with a focus on Ebola outbreak data.

Contribution

It introduces a new model for recognizing dynamic network structures from mobile data, aiding in understanding propagation dynamics in complex networks.

Findings

Network structure significantly affects epidemic spread.

Mobile data can effectively model dynamic network changes.

Proposed recognition model offers insights into propagation mechanisms.

Abstract

Information and epidemic propagation dynamics in complex networks is truly important to discover and control terrorist attack and disease spread. How to track, recognize and model such dynamics is a big challenge. With the popularity of intellectualization and the rapid development of Internet of Things (IoT), massive mobile data is automatically collected by millions of wireless devices (e.g., smart phone and tablet). In this article, as a typical use case, the impact of network structure on epidemic propagation dynamics is investigated by using the mobile data collected from the smart phones carried by the volunteers of Ebola outbreak areas. On this basis, we propose a model to recognize the dynamic structure of a network. Then, we introduce and discuss the open issues and future work for developing the proposed recognition model.