A Greedy Approach for Dynamic Control of Diffusion Processes in Networks

Scaman, Kevin; Kalogeratos, Argyris; Vayatis, Nicolas

doi:10.1109/ictai.2015.99

Cited by 9 publications

(26 citation statements)

References 18 publications

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“…Dynamic Resource Allocation (DRA) [11], [12] is a model for network control, originally developed for SISlike processes [10] (the nodes are either infected or healthy without permanent immunity) that distributes a limited budget of available treatment resources on infected nodes in order to speed-up their recovery. The resources are non-cumulable at nodes (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…Then, the administrator only has to ensure that at each moment the resources will be spent on the infected nodes with the highest scores. Among the proposed options, a simple yet efficient local score is the Largest Reduction in Infectious Edges (LRIE) [11], which depends on the infection state of the neighbors, hence it needs to be updated regularly during the process.…”

Section: Introductionmentioning

confidence: 99%

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Sequential Dynamic Resource Allocation for Epidemic Control

Fekom¹,

Vayatis²,

Kalogeratos³

2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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Under the Dynamic Resource Allocation (DRA) model, an administrator has the mission to allocate dynamically a limited budget of resources to the nodes of a network in order to reduce a diffusion process (DP) (e.g. an epidemic). The standard DRA assumes that the administrator has constantly full information and instantaneous access to the entire network. Towards bringing such strategies closer to real-life constraints, we first present the Restricted DRA model extension where, at each intervention round, the access is restricted to only a fraction of the network nodes, called sample. Then, inspired by sequential selection problems such as the well-known Secretary Problem, we propose the Sequential DRA (SDRA) model. Our model introduces a sequential aspect in the decision process over the sample of each round, offering a completely new perspective to the dynamic DP control. Finally, we incorporate several sequential selection algorithms to SDRA control strategies and compare their performance in SIS epidemic simulations.The authors are with CMLA -ENS Paris-Saclay,

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sequential Dynamic Resource Allocation for Epidemic Control

Fekom¹,

Vayatis²,

Kalogeratos³

2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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“…Previously developed real-time strategies for mitigating contagion on a given network [35,37,38] explored policies that are based on topological characteristics of the graph under the assumption of homogeneous transmission probabilities. The common denominator of existing approaches consists in local interventions which ensure the islanding of infected nodes.…”

Section: E Online Mitigation Of Epidemic Spreadingmentioning

confidence: 99%

“…A less explored direction consists in developing an online policy of assigning a limited remedial budget dynamically based on real-time feedback, also known as a closed-loop control. The impact of vaccination of the largest degree nodes or of those with the largest number of infected neighbors was investigated in [35,36], while an alternative strategy is focused on the largest reduction in infectious edges [37]. Finally, an online policy based on the resolution of the minimal maxcut problem was introduced [38], where optimization is carried out with respect to the expected time to extinction of the SIS epidemic.…”

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confidence: 99%

“…The goal is to deploy the resources optimally so that the total number of infected nodes S(T ) at the final time is minimized. The assumption of a time-distributed budget B µ (t) is highly reasonable due to the restricted vaccine availability.Previously developed real-time strategies for mitigating contagion on a given network [35,37,38] explored policies that are based on topological characteristics of the graph under the assumption of homogeneous transmission probabilities. The common denominator of existing approaches consists in local interventions which ensure the islanding of infected nodes.…”

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confidence: 99%

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Optimal deployment of resources for maximizing impact in spreading processes

Lokhov

Saad

2017

Proc. Natl. Acad. Sci. U.S.A.

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The effective use of limited resources for controlling spreading processes on networks is of prime significance in diverse contexts, ranging from the identification of "influential spreaders" for maximizing information dissemination and targeted interventions in regulatory networks, to the development of mitigation policies for infectious diseases and financial contagion in economic systems. Solutions for these optimization tasks that are based purely on topological arguments are not fully satisfactory; in realistic settings the problem is often characterized by heterogeneous interactions and requires interventions over a finite time window via a restricted set of controllable nodes. The optimal distribution of available resources hence results from an interplay between network topology and spreading dynamics. We show how these problems can be addressed as particular instances of a universal analytical framework based on a scalable dynamic message-passing approach and demonstrate the efficacy of the method on a variety of real-world examples.Spreading corresponds to omnipresent processes describing a vast number of phenomena in social, natural and technological networks [1][2][3][4] whereby information, viruses and failures propagate through their edges via the interactions between individual constituents. Spreading cascades have a huge impact on the modern world, be it negative or positive. An 11 minute power grid disturbance in Arizona and California in 2011 led to cascading outages and left 2.7 million customers without power [5]. As many as 579,000 people around the world could have been killed by the H1N1 influenza pandemic characterized by a rapid spreading through the global transportation networks [6]. The U.S. economy losses from the 2008 financial crisis resulted from cascading bankruptcies of major financial institutions are estimated at the level of $22 trillion [7]. Therefore, it is not surprising that efficient prediction and control of these undesired spreading processes are regarded as fundamental questions of paramount importance in developing policies for optimal placement of cascade-preventing devices in power grid, real-time distribution of vaccines and antidotes to mitigate epidemic spread, regulatory measures in interbanking lending networks and other modern world problems, such as protection of critical infrastructures against cyber-attacks and computer viruses [8].On the other hand, spreading processes can also be considered beneficial. The ice bucket challenge campaign in social networks raised $115 million donations to the ALS association fighting the Amyotrophic Lateral Sclerosis, in particular due to a significant involvement of celebrities acting as "influencers" [9]. In the context of political campaigning, there are already winners [10,11] and losers, and this division is likely to become more pronounced and critical in the future [12]. Winners are those who use communication and social networks effectively to set the opinions of voters or consumers, maximizing the impact of scarce...

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