Key node identification for a network topology using hierarchical comprehensive importance coefficients Article Swipe
Fanshuo Qiu
,
Chengpu Yu
,
Yunji Feng
,
Yao Li
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-3964023/v1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-3964023/v1
Key nodes are similar to important hubs in a network structure, which can directly determine the robustness and stability of the network. By effectively identifying and protecting these critical nodes, the robustness of the network can be improved, making it more resistant to external interference and attacks. There are various topology analysis methods for existing networks, but key node identification methods often only focus on local attributes or global attributes. Designing an algorithm that combines both attributes can improve the accuracy of key node identification. In this paper, the constraint coefficient of a weakly connected network is calculated based on the Salton indicator, and the hierarchical tenacity global coefficient is obtained by an improved K-Shell decomposition method. Then, a hierarchical comprehensive node importance identification algorithm is proposed which can comprehensively indicate the local and global attributes of the network nodes. Experimental results on real network datasets show that the proposed algorithm outperforms the other classic algorithms in terms of connectivity, average remaining edges, sensitivity and monotonicity.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-3964023/v1
- https://www.researchsquare.com/article/rs-3964023/latest.pdf
- OA Status
- green
- Cited By
- 1
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392760788
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4392760788Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-3964023/v1Digital Object Identifier
- Title
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Key node identification for a network topology using hierarchical comprehensive importance coefficientsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-03-13Full publication date if available
- Authors
-
Fanshuo Qiu, Chengpu Yu, Yunji Feng, Yao LiList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-3964023/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-3964023/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.researchsquare.com/article/rs-3964023/latest.pdfDirect OA link when available
- Concepts
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Key (lock), Node (physics), Identification (biology), Topology (electrical circuits), Computer science, Network topology, Computer network, Mathematics, Engineering, Computer security, Combinatorics, Botany, Biology, Structural engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- References (count)
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31Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.edges, | 163 |
| abstract_inverted_index.global | 69, 108, 135 |
| abstract_inverted_index.making | 39 |
| abstract_inverted_index.nodes, | 30 |
| abstract_inverted_index.nodes. | 140 |
| abstract_inverted_index.paper, | 88 |
| abstract_inverted_index.weakly | 94 |
| abstract_inverted_index.K-Shell | 115 |
| abstract_inverted_index.average | 161 |
| abstract_inverted_index.classic | 155 |
| abstract_inverted_index.improve | 79 |
| abstract_inverted_index.method. | 117 |
| abstract_inverted_index.methods | 53, 61 |
| abstract_inverted_index.network | 10, 35, 96, 139, 145 |
| abstract_inverted_index.results | 142 |
| abstract_inverted_index.similar | 4 |
| abstract_inverted_index.various | 50 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.accuracy | 81 |
| abstract_inverted_index.analysis | 52 |
| abstract_inverted_index.attacks. | 47 |
| abstract_inverted_index.combines | 75 |
| abstract_inverted_index.critical | 29 |
| abstract_inverted_index.datasets | 146 |
| abstract_inverted_index.directly | 14 |
| abstract_inverted_index.existing | 55 |
| abstract_inverted_index.external | 44 |
| abstract_inverted_index.improved | 114 |
| abstract_inverted_index.indicate | 131 |
| abstract_inverted_index.network. | 22 |
| abstract_inverted_index.obtained | 111 |
| abstract_inverted_index.proposed | 127, 150 |
| abstract_inverted_index.tenacity | 107 |
| abstract_inverted_index.topology | 51 |
| abstract_inverted_index.Designing | 71 |
| abstract_inverted_index.algorithm | 73, 125, 151 |
| abstract_inverted_index.connected | 95 |
| abstract_inverted_index.determine | 15 |
| abstract_inverted_index.important | 6 |
| abstract_inverted_index.improved, | 38 |
| abstract_inverted_index.networks, | 56 |
| abstract_inverted_index.remaining | 162 |
| abstract_inverted_index.resistant | 42 |
| abstract_inverted_index.stability | 19 |
| abstract_inverted_index.algorithms | 156 |
| abstract_inverted_index.attributes | 67, 77, 136 |
| abstract_inverted_index.calculated | 98 |
| abstract_inverted_index.constraint | 90 |
| abstract_inverted_index.importance | 123 |
| abstract_inverted_index.indicator, | 103 |
| abstract_inverted_index.protecting | 27 |
| abstract_inverted_index.robustness | 17, 32 |
| abstract_inverted_index.structure, | 11 |
| abstract_inverted_index.attributes. | 70 |
| abstract_inverted_index.coefficient | 91, 109 |
| abstract_inverted_index.effectively | 24 |
| abstract_inverted_index.identifying | 25 |
| abstract_inverted_index.outperforms | 152 |
| abstract_inverted_index.sensitivity | 164 |
| abstract_inverted_index.Experimental | 141 |
| abstract_inverted_index.hierarchical | 106, 120 |
| abstract_inverted_index.interference | 45 |
| abstract_inverted_index.comprehensive | 121 |
| abstract_inverted_index.connectivity, | 160 |
| abstract_inverted_index.decomposition | 116 |
| abstract_inverted_index.monotonicity. | 166 |
| abstract_inverted_index.identification | 60, 124 |
| abstract_inverted_index.comprehensively | 130 |
| abstract_inverted_index.identification. | 85 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5018395060 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I125839683 |
| citation_normalized_percentile.value | 0.55279009 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |