Reviving a failed network through microscopic interventions

Hillel Sanhedrai, Jianxi Gao, Amir Bashan, Moshe Schwartz, Shlomo Havlin, Baruch Barzel

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

From mass extinction to cell death, complex networked systems often exhibit abrupt dynamic transitions between desirable and undesirable states. These transitions are often caused by topological perturbations (such as node or link removal, or decreasing link strengths). The problem is that reversing the topological damage, namely, retrieving lost nodes or links or reinforcing weakened interactions, does not guarantee spontaneous recovery to the desired functional state. Indeed, many of the relevant systems exhibit a hysteresis phenomenon, remaining in the dysfunctional state, despite reconstructing their damaged topology. To address this challenge, we develop a two-step recovery scheme: first, topological reconstruction to the point where the system can be revived and then dynamic interventions to reignite the system’s lost functionality. By applying this method to a range of nonlinear network dynamics, we identify the recoverable phase of a complex system, a state in which the system can be reignited by microscopic interventions, for instance, controlling just a single node. Mapping the boundaries of this dynamical phase, we obtain guidelines for our two-step recovery.

Original languageEnglish
Pages (from-to)338-349
Number of pages12
JournalNature Physics
Volume18
Issue number3
DOIs
StatePublished - Mar 2022

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Nature Limited.

Funding

H.S. acknowledges the support of the Presidential Fellowship of Bar-Ilan University, Israel, and the Mordecai and Monique Katz Graduate Fellowship Program. This research was supported by the Israel Science Foundation (grant nos. 499/19 and 189/19), the US National Science Foundation CRISP award (grant no. 1735505), the Bar-Ilan University Data Science Institute grant for research on network dynamics, the ISF-NSFC joint research program (grant nos. 3132/19 and 3552/21), the US–Israel NSF–BSF programme (grant no. 2019740), the EU H2020 project RISE (grant no. 821115), the EU H2020 project DIT4TRAM (grant no. 953783), the Defense Threat Reduction Agency (DTRA grant no. HDTRA-1-19-1-0016), the US National Science Foundation (grant no. 2047488) and the Rensselaer-IBM AI Research Collaboration. H.S. acknowledges the support of the Presidential Fellowship of Bar-Ilan University, Israel, and the Mordecai and Monique Katz Graduate Fellowship Program. This research was supported by the Israel Science Foundation (grant nos. 499/19 and 189/19), the US National Science Foundation CRISP award (grant no. 1735505), the Bar-Ilan University Data Science Institute grant for research on network dynamics, the ISF-NSFC joint research program (grant nos. 3132/19 and 3552/21), the US?Israel NSF?BSF programme (grant no. 2019740), the EU H2020 project RISE (grant no. 821115), the EU H2020 project DIT4TRAM (grant no. 953783), the Defense Threat Reduction Agency (DTRA grant no. HDTRA-1-19-1-0016), the US National Science Foundation (grant no. 2047488) and the Rensselaer-IBM AI Research Collaboration.

FundersFunder number
Bar-Ilan University Data Science Institute
EU H2020821115, 953783
ISF-NSFC3132/19, 3552/21
US?Israel NSF?BSF programme
US–Israel NSF
National Science Foundation
Directorate for Social, Behavioral and Economic Sciences1735505
Defense Threat Reduction Agency2047488, HDTRA-1-19-1-0016
United States-Israel Binational Science Foundation2019740
Bar-Ilan University
Israel Science Foundation189/19, 499/19

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