Evidence map›Paper›PMID 28986539›Full record

ArticleScientific reports2017

Enhancing robustness of interdependent network under recovery based on a two-layer-protection strategy.

Maoguo Gong, Yixing Wang, Shanfeng Wang, Wenfeng Liu

Abstract read
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Maoguo GongKey Lab of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi'an, Shaanxi Province, 710071, China. gong@ieee.org.
Yixing WangKey Lab of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi'an, Shaanxi Province, 710071, China.
Shanfeng WangKey Lab of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi'an, Shaanxi Province, 710071, China.
Wenfeng LiuKey Lab of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi'an, Shaanxi Province, 710071, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The robustness of coupled networks has attracted great attention recently, because the spread of failures from one network to its coupled network makes the coupled networks more vulnerable. Most existing achievements mainly focused on the integrity properties of coupled networks. However, failures also exist when networks are being reconstructed. Moreover, existing node-protection methods which aim to enhance the robustness of coupled networks only protect the influential nodes in one layer. In this paper, firstly, a two-layer-protection strategy is proposed to enhance the robustness of coupled networks under their reconstruction. Secondly, we adopt five strategies based on different centralities to select influential nodes, and propose a two-layer vision for each of them. Lastly, experiments on three different coupled networks show that by applying the two-layer-protection strategy, the robustness of coupled networks can be enhanced more efficiently compared with other methods which only protect nodes in one layer.

Identifiers

PMID28986539
PMCPMC5630635

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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.