Evidence map›Paper›PMID 26612009›Full record

ArticleScientific reports2015

Extreme events in multilayer, interdependent complex networks and control.

Yu-Zhong Chen, Zi-Gang Huang, Hai-Feng Zhang, Daniel Eisenberg, Thomas P Seager, Ying-Cheng Lai

Abstract read
In one paragraph

Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Yu-Zhong ChenSchool of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona 85287, USA.
Zi-Gang HuangSchool of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona 85287, USA.
Hai-Feng ZhangSchool of Mathematical Science, Anhui University, Hefei 230039, China.
Daniel EisenbergSchool of Sustainable Engineering and Built Environment, Arizona State University, Tempe, AZ 85287, USA.
Thomas P SeagerSchool of Sustainable Engineering and Built Environment, Arizona State University, Tempe, AZ 85287, USA.
Ying-Cheng LaiSchool of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona 85287, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We investigate the emergence of extreme events in interdependent networks. We introduce an inter-layer traffic resource competing mechanism to account for the limited capacity associated with distinct network layers. A striking finding is that, when the number of network layers and/or the overlap among the layers are increased, extreme events can emerge in a cascading manner on a global scale. Asymptotically, there are two stable absorption states: a state free of extreme events and a state of full of extreme events, and the transition between them is abrupt. Our results indicate that internal interactions in the multiplex system can yield qualitatively distinct phenomena associated with extreme events that do not occur for independent network layers. An implication is that, e.g., public resource competitions among different service providers can lead to a higher resource requirement than naively expected. We derive an analytical theory to understand the emergence of global-scale extreme events based on the concept of effective betweenness. We also articulate a cost-effective control scheme through increasing the capacity of very few hubs to suppress the cascading process of extreme events so as to protect the entire multi-layer infrastructure against global-scale breakdown.

Identifiers

PMID26612009
PMCPMC4661526

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.