Evidence map›Paper›PMID 38275487›Full record

ArticleEntropy (Basel, Switzerland)2023

Improving Robustness of High-Low-Order Coupled Networks against Malicious Attacks Based on a Simulated Annealing Algorithm.

Chengjun Zhang, Yifan Xie, Yadang Chen, Wenbin Yu, Gaofeng Xiang, Peijun Zhao, Yi Lei

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2023. 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

7 authors.

Chengjun ZhangSchool of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China.ORCID 0000-0002-4458-5843
Yifan XieSchool of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China.
Yadang ChenSchool of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China.ORCID 0000-0002-4448-2617
Wenbin YuWuxi Institute of Technology, Nanjing University of lnformation Science & Technology, Wuxi 214000, China.ORCID 0000-0003-4786-4036
Gaofeng XiangSchool of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China.
Peijun ZhaoSchool of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China.
Yi LeiSchool of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China.ORCID 0000-0001-7395-0873

Funding

National Natural Science Foundation of China 62071240
6 · The paper itself

Abstract

Malicious attacks can cause significant damage to the structure and functionality of complex networks. Previous research has pointed out that the ability of networks to withstand malicious attacks becomes weaker when networks are coupled. However, traditional research on improving the robustness of networks has focused on individual low-order or higher-order networks, lacking studies on coupled networks with higher-order and low-order networks. This paper proposes a method for optimizing the robustness of coupled networks with higher-order and low-order based on a simulated annealing algorithm to address this issue. Without altering the network's degree distribution, the method rewires the edges, taking the robustness of low-order and higher-order networks as joint optimization objectives. Making minimal changes to the network, the method effectively enhances the robustness of coupled networks. Experiments were conducted on Erdős-Rényi random networks (ER), scale-free networks (BA), and small-world networks (SW). Finally, validation was performed on various real networks. The results indicate that this method can effectively enhance the robustness of coupled networks with higher-order and low-order.

Indexed as

coupled networksmalicious attacksrobustnesssimulated annealing algorithm

Identifiers

PMID38275487
PMCPMC10814750

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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.