Multi-Stage Sparse Resource Allocation for Control of Spreading Processes over Networks

Somers, Vera L. J.; Manchester, Ian R.

doi:10.23919/acc53348.2022.9867834

Cited by 3 publications

(2 citation statements)

References 41 publications

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“…The second inequality in (23) follows because p is non-negative and A K ≥ A k . In the proof of Proposition 4 we obtained (7) from ( 6) and similarly (23) implies (25) For the corresponding convex formulation we refer to equation ( 21) in Somers and Manchester (2022b).…”

Section: Infinite Timementioning

confidence: 93%

“…In this paper, we therefore, present a framework for dynamic, sparse resource allocation on time-varying networks for control of spreading processes. This framework builds on work presented in Somers and Manchester (2022b), where we presented a multi-stage sparse resource allocation method for static networks. The presented framework distinguishes itself by extending this work to temporalswitching networks and hence, overcoming the shortcomings of the assumption of a static network.…”

Section: Introductionmentioning

confidence: 99%

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Sparse Resource Allocation for Spreading Processes on Temporal-Switching Networks

Somers¹,

Manchester²

2023

Preprint

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Spreading processes, e.g. epidemics, wildfires and rumors, are often modeled on static networks. However, their underlying networks structures, e.g changing contacts in social networks, different weather forecasts for wildfires, are due to ever changing circumstances inherently time-varying in nature. In this paper, we therefore, propose an optimization framework for sparse resource allocation for control of spreading processes over temporal networks with known connectivity patterns. We use convex optimization, in particular exponential cone programming, and dynamic programming techniques to bound and minimize the risk of an undetected outbreak by allocating budgeted resources each time step. We demonstrate with misinformation, epidemic and wildfire examples how the method can provide targeted allocation of resources.

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Section: Infinite Timementioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Sparse Resource Allocation for Spreading Processes on Temporal-Switching Networks

Somers¹,

Manchester²

2023

Preprint

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Fixation probability in evolutionary dynamics on switching temporal networks

Bhaumik,

Masuda

2023

J. Math. Biol.

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Population structure has been known to substantially affect evolutionary dynamics. Networks that promote the spreading of fitter mutants are called amplifiers of selection, and those that suppress the spreading of fitter mutants are called suppressors of selection. Research in the past two decades has found various families of amplifiers while suppressors still remain somewhat elusive. It has also been discovered that most networks are amplifiers of selection under the birth-death updating combined with uniform initialization, which is a standard condition assumed widely in the literature. In the present study, we extend the birth-death processes to temporal (i.e., time-varying) networks. For the sake of tractability, we restrict ourselves to switching temporal networks, in which the network structure deterministically alternates between two static networks at constant time intervals or stochastically in a Markovian manner. We show that, in a majority of cases, switching networks are less amplifying than both of the two static networks constituting the switching networks. Furthermore, most small switching networks, i.e., networks on six nodes or less, are suppressors, which contrasts to the case of static networks.

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