Evidence map›Paper›PMID 36673163›Full record

ArticleEntropy (Basel, Switzerland)2022

Fragility Induced by Interdependency of Complex Networks and Their Higher-Order Networks.

Chengjun Zhang, Yi Lei, Xinyu Shen, Qi Li, Hui Yao, Di Cheng, Yifan Xie, Wenbin Yu

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

8 authors.

Chengjun ZhangSchool of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China.ORCID 0000-0002-4458-5843
Yi LeiSchool of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China.ORCID 0000-0001-7395-0873
Xinyu ShenSchool of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China.ORCID 0000-0001-5862-376X
Qi LiSchool of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China.ORCID 0000-0002-2876-725X
Hui YaoSchool of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China.ORCID 0000-0002-3967-9511
Di ChengSchool of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China.ORCID 0000-0002-3245-0095
Yifan XieSchool of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China.ORCID 0000-0002-1839-0145
Wenbin YuSchool of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China.ORCID 0000-0003-4786-4036

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The higher-order structure of networks is a hot research topic in complex networks. It has received much attention because it is closely related to the functionality of networks, such as network transportation and propagation. For instance, recent studies have revealed that studying higher-order networks can explore hub structures in transportation networks and information dissemination units in neuronal networks. Therefore, the destruction of the connectivity of higher-order networks will cause significant damage to network functionalities. Meanwhile, previous works pointed out that the function of a complex network depends on the giant component of the original(low-order) network. Therefore, the network functionality will be influenced by both the low-order and its corresponding higher-order network. To study this issue, we build a network model of the interdependence of low-order and higher-order networks (we call it ILH). When some low-order network nodes fail, the low-order network's giant component shrinks, leading to changes in the structure of the higher-order network, which further affects the low-order network. This process occurs iteratively; the propagation of the failure can lead to an eventual network crash. We conducted experiments on different networks based on the percolation theory, and our network percolation results demonstrated a first-order phase transition feature. In particular, we found that an ILH is more fragile than the low-order network alone, and an ILH is more likely to be corrupted in the event of a random node failure.

Indexed as

cascading failurefirst-order phase transitionhigher-order organizationinterdependent networks

Identifiers

PMID36673163
PMCPMC9858052

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.