Epidemic spreading on coupling network with higher-order information layer

Abstract

Epidemic tends to break out with information spreading which occurs between pairwise individuals or in groups. In active social online platform, three or more individuals can share information or exchange opinion, which could be modeled as a clique beyond pairwise interaction. This work studies the influence of information with higher-order cliques whose closure probability is described by higher-order clustering coefficient on epidemic spreading. The coupled spreading process of disease and awareness follows an unaware-aware-unaware-susceptible-infected-susceptible (UAU-SIS) model on multiplex networks. We deduce the epidemic threshold of multiplex network with higher-order cliques, and explore the effects of cliques on epidemic spreading process at slow, middle and fast pairwise information spreading rates. The results show that the epidemic threshold and higher-order clustering coefficient are positively correlated. Moreover, we find that the effect of higher-order cliques could be ignored when pairwise information spreading rate is large enough. Finally, we show that the steady infection ratio decreases with the increase of the higher-order clustering coefficient.

Measuring the significance of higher-order dependency in networks

“…As a result, traditional network modeling techniques only provide a limited representation of complex systems. In contrast, higher-order network models (HONs) that better capture many-body interactions can improve the analysis of various network analysis tasks [11][12][13][14][15][16][17]. Recent work has developed four different lines of modeling approaches to embed higher-order dependencies into HON models, including hypergraph models [11,[18][19][20][21][22], simplicial complex models [16,[23][24][25][26][27][28][29][30], motif-based higher-order models [31][32][33][34][35][36][37][38] and higher-order Markov models [5,6,9,[39][40][41][42][43].…”

Optimizing information-driven awareness allocation for controlling activity-triggered epidemic spread

“…Also, evolutionary game theory provides a powerful framework to study how individual behaviors, such as vaccination strategies and information dissemination, evolve over time and impact epidemic dynamics [30,31]. In tandem with these investigations, the scope of work in this field has expanded to encompass real and complex epidemic spreading modes [20], awareness diffusion mechanisms [32], and multi-layer structures [33,34], elevating the coevolution dynamics of epidemics and awareness to a prominent topic of exploration.…”