3 research outputs found
Temporal network sparsity and the slowing down of spreading
Interactions in time-varying complex systems are often very heterogeneous at
the topological level (who interacts with whom) and at the temporal level (when
interactions occur and how often). While it is known that temporal
heterogeneities often have strong effects on dynamical processes, e.g. the
burstiness of contact sequences is associated with slower spreading dynamics,
the picture is far from complete. In this paper, we show that temporal
heterogeneities result in temporal sparsity} at the time scale of average
inter-event times, and that temporal sparsity determines the amount of slowdown
of Susceptible-Infectious (SI) spreading dynamics on temporal networks. This
result is based on the analysis of several empirical temporal network data
sets. An approximate solution for a simple network model confirms the
association between temporal sparsity and slowdown of SI spreading dynamics.
Since deterministic SI spreading always follows the fastest temporal paths, our
results generalize -- paths are slower to traverse because of temporal
sparsity, and therefore all dynamical processes are slower as well
Recommended from our members
An Innovative Multi-Objective Rescheduling System for Mitigating Pandemic Spread in Aviation Networks
Data Availability Statement
The data presented in this study are available online at: https://www.mdpi.com/2571-8797/6/1/6#app1-cleantechnol-06-00006 .The novel coronavirus outbreak has significantly heightened environmental costs and operational challenges for civil aviation airlines, prompting emergency airport closures in affected regions and a substantial decline in ridership. The consequential need to reassess, delay, or cancel flight itineraries has led to disruptions at airports, amplifying the risk of disease transmission. In response, this paper proposes a spatial approach to efficiently address pandemic spread in the civil aviation network. The methodology prioritizes the use of a static gravity model for calculating route-specific infection pressures, enabling strategic flight rescheduling to control infection levels at airports (nodes) and among airlines (edges). Temporally, this study considers intervals between takeoffs and landings to minimize crowd gatherings, mitigating the novel coronavirus transmission rate. By constructing a discrete space–time network for irregular flights, this research generates a viable set of routes for aircraft operating in special circumstances, minimizing both route-specific infection pressures and operational costs for airlines. Remarkably, the introduced method demonstrates substantial savings, reaching almost 53.4%, compared to traditional plans. This showcases its efficacy in optimizing responses to pandemic-induced disruptions within the civil aviation network, offering a comprehensive solution that balances operational efficiency and public health considerations in the face of unprecedented challenges.National Natural Science Foundation of China (62206062, U2034208 and 2022YFC3002502); China Railway P2023X012