Evidence map›Paper›PMID 41492467›Full record

ArticleiScience2026

Multilayer innovation network resilience: A framework for digital economy vulnerability assessment.

Hongjuan Zhang, Haibing Liu, Rongkai Chen

Abstract read
In one paragraph

Article in iScience, 2026. 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
–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

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

3 authors.

Hongjuan ZhangSchool of Management, Wuhan University of Science and Technology, Wuhan 430065, China.
Haibing LiuSchool of Management, Wuhan University of Science and Technology, Wuhan 430065, China.
Rongkai ChenSchool of Applied Economics, Renmin University of China, Beijing 100872, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Innovation networks drive technological progress, yet their multilayer structures remain poorly understood in digital economy vulnerability contexts. This study develops a comprehensive framework for assessing multilayer innovation network resilience, analyzing interdependencies and disruption scenarios using China's digital economy as a representative empirical context. We construct coupled multilayer networks implementing four integrated attack strategies to identify cascading vulnerability mechanisms. Results reveal asymmetric patterns: collaboration networks show significant fragility to targeted attacks, while knowledge networks demonstrate higher resilience, especially during mature stages. Cascade failure analysis establishes that knowledge network disruptions propagate severe ecosystem-wide effects, whereas collaboration network perturbations generate limited cross-layer impacts. This asymmetry advances multilayer innovation network theory and provides practical insights for vulnerability assessment. The framework indicates that protecting critical technological knowledge should prioritize over maintaining collaborative arrangements when resources are limited, as knowledge networks constitute the essential integrative mechanism within innovation systems.

Indexed as

EconomicsEnvironmental science

Identifiers

PMID41492467
PMCPMC12765178

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.