Abstract
Mobility is a critical element for understanding human contact networks. In many studies, the researchers use random processes to model human mobility. However, people do not move randomly in their environment. Their interactions do not depend only on spatial constraints but on their temporal, social, economic, and cultural activities. The topological structure of the physical and/or proximity contact networks depends, therefore, entirely on the mobility patterns. This paper performs an extensive comparative analysis of real-world temporal contact networks and synthetic networks based on influential mobility models. Results show that the various topological properties of most of the synthetic datasets depart from those observed in real-world contact networks because the randomness of some mobility parameters tends to move away from human contact properties. However, it appears that data generated using Spatio-Temporal Parametric Stepping (STEPS) mobility model reveals similarities with real temporal contact networks such as heavy-tailed distribution of contact duration, frequency of pairs of contacts, and the bursty phenomenon. These results pave the way for further improvement of mobility models to generate meaningful artificial contact networks.
Highlights
The evolution of contact network topology is of prime interest for understanding internal dynamics such as information diffusion, opinion, rumor spreading, and disease propagation
One can conclude that mobility models are not sophisticated enough to describe human mobility behavior accurately
It appears that the temporal contact networks generated using the Spatio-Temporal Parametric Stepping (STEPS) mobility models exhibit the most similar properties to real-world temporal contact networks
Summary
The evolution of contact network topology is of prime interest for understanding internal dynamics such as information diffusion, opinion, rumor spreading, and disease propagation. We examine the main properties of human contact networks based on real-world data compared to artificial contact networks based on synthetic mobility data. Our goal is to better understand the similarities and dissimilarities between these two types of networks For this purpose, we consider real contact datasets from the Sociopattern project [7]–[9], the Copenhagen Networks Study (CNS) [10], and the traces generated by four synthetic mobility models (Random Waypoint [11], [12], Gauss-Markov model [13], [14], Truncated Lévy Walk [15] and Spatio-Temporal Parametric Stepping [16]).To perform a comparative study. We perform a comparative analysis of real-world temporal contact networks with each other to get a clear idea about their main common properties. We summarize the main findings and discuss the future direction of this work in section VI, and conclude
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
