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A hybrid fault detection algorithm with the [formula omitted]-good-neighbor pattern and its applications.

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Title: A hybrid fault detection algorithm with the [formula omitted]-good-neighbor pattern and its applications.
Authors: Wang, Zhihang1,2 (AUTHOR), Liu, Jiafei1,2 (AUTHOR) liujiafei@gxnu.edu.cn, Hsieh, Sun-Yuan3 (AUTHOR)
Source: Discrete Applied Mathematics. Jan2026, Vol. 378, p366-376. 11p.
Subjects: Fault diagnosis, Hypercube networks (Computer networks), Algorithms, Validity of statistics, Computer network reliability
Abstract: Fault diagnosis has been a key learning paradigm, supporting a wide range of tasks such as network reliability, wafer test, and data center network. However, the accuracy of fault detection depends on the underlying topology of interconnection networks. While existing diagnostic schemes have made significant progress in node failures, addressing the challenges imposed by distinct fault patterns. In many real network scenarios, the presence of faults usually exhibits characteristics such as complexity and heterogeneity, where the communication links between processors may be faulty. To tackle these challenges, we develop a hybrid g -good-neighbor fault diagnosis scheme. First, we establish the g -good-neighbor diagnosability of the hypercube network with missing edges and broken-down nodes. Besides, we present an intelligent hybrid fault perception algorithm (for short IHFP) to identify all faulty nodes and faulty edges. Subsequently, we apply this algorithm to hypercube as well as real flight network. Finally, we verify the efficiency and correctness of the proposed algorithm in terms of Precision, Recall, F1 Score and Accuracy. [ABSTRACT FROM AUTHOR]
Copyright of Discrete Applied Mathematics is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 188801568
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  Data: <searchLink fieldCode="JN" term="%22Discrete+Applied+Mathematics%22">Discrete Applied Mathematics</searchLink>. Jan2026, Vol. 378, p366-376. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Hypercube+networks+%28Computer+networks%29%22">Hypercube networks (Computer networks)</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Validity+of+statistics%22">Validity of statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+network+reliability%22">Computer network reliability</searchLink>
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  Data: Fault diagnosis has been a key learning paradigm, supporting a wide range of tasks such as network reliability, wafer test, and data center network. However, the accuracy of fault detection depends on the underlying topology of interconnection networks. While existing diagnostic schemes have made significant progress in node failures, addressing the challenges imposed by distinct fault patterns. In many real network scenarios, the presence of faults usually exhibits characteristics such as complexity and heterogeneity, where the communication links between processors may be faulty. To tackle these challenges, we develop a hybrid g -good-neighbor fault diagnosis scheme. First, we establish the g -good-neighbor diagnosability of the hypercube network with missing edges and broken-down nodes. Besides, we present an intelligent hybrid fault perception algorithm (for short IHFP) to identify all faulty nodes and faulty edges. Subsequently, we apply this algorithm to hypercube as well as real flight network. Finally, we verify the efficiency and correctness of the proposed algorithm in terms of Precision, Recall, F1 Score and Accuracy. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Discrete Applied Mathematics is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1016/j.dam.2025.08.015
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      – Code: eng
        Text: English
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        PageCount: 11
        StartPage: 366
    Subjects:
      – SubjectFull: Fault diagnosis
        Type: general
      – SubjectFull: Hypercube networks (Computer networks)
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Validity of statistics
        Type: general
      – SubjectFull: Computer network reliability
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      – TitleFull: A hybrid fault detection algorithm with the [formula omitted]-good-neighbor pattern and its applications.
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            – D: 15
              M: 01
              Text: Jan2026
              Type: published
              Y: 2026
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