Optimal deployment of resources for maximizing impact in spreading processes
Abstract
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 in a dynamic fashion 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. Here, 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.
- Authors:
-
- Center for Nonlinear Studies and Theoretical Division T-4, Los Alamos National Laboratory, Los Alamos, NM 87545,
- The Nonlinearity and Complexity Research Group, Aston University, Birmingham B4 7ET, United Kingdom
- Publication Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1389879
- Alternate Identifier(s):
- OSTI ID: 1392871
- Report Number(s):
- LA-UR-16-26490
Journal ID: ISSN 0027-8424
- Grant/Contract Number:
- AC52-06NA25396
- Resource Type:
- Published Article
- Journal Name:
- Proceedings of the National Academy of Sciences of the United States of America
- Additional Journal Information:
- Journal Name: Proceedings of the National Academy of Sciences of the United States of America Journal Volume: 114 Journal Issue: 39; Journal ID: ISSN 0027-8424
- Publisher:
- Proceedings of the National Academy of Sciences
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; 60 APPLIED LIFE SCIENCES; Mathematics
Citation Formats
Lokhov, Andrey Y., and Saad, David. Optimal deployment of resources for maximizing impact in spreading processes. United States: N. p., 2017.
Web. doi:10.1073/pnas.1614694114.
Lokhov, Andrey Y., & Saad, David. Optimal deployment of resources for maximizing impact in spreading processes. United States. https://doi.org/10.1073/pnas.1614694114
Lokhov, Andrey Y., and Saad, David. Tue .
"Optimal deployment of resources for maximizing impact in spreading processes". United States. https://doi.org/10.1073/pnas.1614694114.
@article{osti_1389879,
title = {Optimal deployment of resources for maximizing impact in spreading processes},
author = {Lokhov, Andrey Y. and Saad, David},
abstractNote = {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 in a dynamic fashion 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. Here, 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.},
doi = {10.1073/pnas.1614694114},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
number = 39,
volume = 114,
place = {United States},
year = {Tue Sep 12 00:00:00 EDT 2017},
month = {Tue Sep 12 00:00:00 EDT 2017}
}
https://doi.org/10.1073/pnas.1614694114
Web of Science
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