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Lecture on "Directionality Reduces the Impact of Epidemics in Multilayer Networks"
DateandTime: 2019-10-22 20:36:44

Speaker:Wang Xiangrong, Associate Research Fellow, Southern University of Science and Technology.

Date:Oct. 29, 2019

Time: 9:30 a.m.

Location:1032 Lecture Hall, Block B, Zhixin Building, Central Campus

Inviter:Prof. Wang Guanghui, the School of Mathematics

Sponsor:the School of Mathematics


The study of how diseases spread has greatly benefited from advances in network modeling. Recently, a class of networks known as multilayer graphs has been shown to describe more accurately many real systems, making it possible to address more complex scenarios in epidemiology such as the interaction between different pathogens or multiple strains of the same disease. In this work, we study in depth a class of networks that have gone unnoticed up to now, despite of its relevance for spreading dynamics. Specifically, we focus on directed multilayer networks, characterized by the existence of directed links, either within the layers or across layers. Using the generating function approach and numerical simulations of a stochastic susceptible-infected-susceptible (SIS) model, we calculate the epidemic threshold for a real-world multilayer network composed by users of two different social platforms: friendfeed and twitter. Besides, we analyze several combinations of directionality: (i) Directed layer - Undirected interlinks - Directed interlinks (DUD); (ii) Directed layer - Directed interlinks - Directed layer (DDD); (iii) Undirected layer -Directed interlinks - Undirected layer (UDU), and the standard scenario for the sake of comparison, namely, (iv) Undirected layer - Undirected interlinks - Undirected layer (UUU). Our results show that the main feature that determines the value of the epidemic threshold is the directionality of the links connecting different layers. Our findings are of utmost interest given the ubiquitous presence of directed multilayer networks and the widespread use of disease-like spreading processes in a broad range of phenomena such as diffusion processes in social and transportation systems.


Dr. Wang Xiangrong is currently associate research fellow at Southern University of Science and Technology. He studied in the Faculty of Electrical Engineering, Mathematics and Computer Science in the Delft University of Technology from 2012 to 2016. Studying under the supervision of network science expert Prof. Piet Van Mieghem, he completed his Ph.D. in 2016. From March, 2017 to November, 2018, he engaged in postdoctoral research under the supervision of network science expert Prof. Yamir Moreno. His main research interests include network science, data science, network robustness, nonlinear dynamics and network spectrum theory. He has published 12 papers as lead author in top journals such asNew Journal of PhysicsandPhysical Review E.

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Edited by: Wang Tongtong

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