It is shown that spreading dynamics on temporal networks can analogously be mapped to reachability in temporal event graphs, which provides a theoretical and computational framework for a class of processes, such as variants of the susceptible-infected-susceptible model.
Abstract
The physics of spreading in static networks is well understood through mappings to percolation. We show that spreading dynamics on temporal networks can analogously be mapped to reachability in temporal event graphs. This provides a theoretical and computational framework for a class of processes, such as variants of the susceptible-infected-susceptible model. Without explicit simulations, through the component analysis of event graphs, we obtain epidemic prevalence and derive epidemic thresholds for temporal networks with arbitrary degree and inter-event time distributions, with significant computational advantages as compared to explicit simulations.
In this paper, we investigate a discrete-time SIS epidemic model and the epidemic thresholds on complex networks. We focus on proposing a community-level epidemic threshold set and establishing a comparative result between the local epidemic thresholds and the global epidemic threshold. To verify our theoretical findings and structural properties, we conduct numerical experiments on one synthetic network (Network1) and one real-world network (the Haslemere contact network). Our numerical simulations, along with the computation and statistical ranking of the epidemic threshold sets, align accurately with our theoretical results.
In this paper, we explore the dynamics of epidemic processes on different types of single-layer network structures, emphasizing the impact of network structure on the spread of disease. We first propose a single-layer SIR (susceptible-infected-recovered) network model and investigate the impact of network structure on virus transmission. Numerical simulation results indicate that in scale-free networks, infections in hub nodes lead to faster and more widespread spread compared to the absence of such a network. In terms of epidemic control, the importance of disconnecting key nodes is emphasized. In random networks, transmission is generally faster and has higher peak infection levels than in scale-free networks. The findings reveal that network topology and initial infection nodes profoundly influence virus spread patterns, offering critical insights for designing targeted epidemic control strategies that minimize transmission by breaking key network links.
Li Yike, E. Gubar· Contributions to Game Theory...· 0 citations
Extreme epidemic risk is controlled by the right tail of the outbreak-size distribution, but this distribution is generally unknown for non-Markovian spreading on networks. Here we determine this distribution by mapping non-Markovian SIR dynamics to an effective Markovian description. We show that arbitrary infection and recovery time statistics can be incorporated through a single edge transmissibility, yielding an effective Markovian process that reproduces the full outbreak-size statistics. For weakly heterogeneous networks, the reduction yields a universal well-mixed semiclassical theory governed by the bond-percolation reproductive number. Outbreak statistics across diverse waiting-time distributions and topologies collapse onto one predictive curve. For highly heterogeneous and empirical networks, the corresponding effective Markovian dynamics on the network captures the complete distribution. Our results provide a direct route from measured waiting-time distributions to quantitative predictions of network-level extreme-outbreak risk.
This work proposes a community-based preferential attachment hypergraph model with tunable modularity and a heavy-tailed degree distribution, reproducing key structural properties in real systems, and develops a hypergraph-based SAIR framework to describe epidemic dynamics with asymptomatic transmission.
This work proposes a general framework for model selection in binary-state spreading processes on networks and shows that asymptotic approximations in the thermodynamic limit can accurately predict inference outcomes in finite systems.
Javier Ureña-Carrión, Tiago P. Peixoto, G. Íñiguez· 0 citations