HeteroRank: Adaptive node ranking in heterogeneous networks incorporating a percolation-based switching mechanism.
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| Title: | HeteroRank: Adaptive node ranking in heterogeneous networks incorporating a percolation-based switching mechanism. |
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| Authors: | Gao, Mengxiao1 (AUTHOR), Song, Yu-Rong1 (AUTHOR) songyr@njupt.edu.cn, Song, Bo2 (AUTHOR) |
| Source: | Physica A. Aug2026, Vol. 695, pN.PAG-N.PAG. 1p. |
| Subjects: | First-order phase transitions |
| Abstract: | In heterogeneous networks with complex topologies, non-uniform connection patterns and diversity in node types limit the effectiveness of traditional centrality measures in node importance ranking. To address this issue, this paper proposes an adaptive node ranking method for heterogeneous networks incorporating a percolation switching mechanism, termed HeteroRank. The proposed method deeply couples node ranking with the network dismantling process by constructing a collaborative dismantling index to evaluate the role of nodes in maintaining global structural stability. By analyzing the topological differences between residual networks under varying removal proportions, the percolation threshold of network collapse is precisely identified. This threshold not only characterizes the critical phase transition point of the network topology but also serves as an adaptive switching mechanism, driving the dynamic evolution of the ranking strategy from global structural dismantling to local dominance evaluation. Extensive experiments demonstrate that this adaptive framework, which dynamically adjusts strategies based on topological phase transitions, significantly outperforms various baseline methods in terms of network dismantling efficiency, connectivity destruction capability, and suppression of spreading processes. These results validate the effectiveness and robustness of HeteroRank in identifying key nodes and implementing targeted interventions in heterogeneous networks. • Adaptive ranking coupled with network dismantling. • Collaborative score evaluates structural stability roles. • Percolation Threshold identifies critical topological transitions. • Ranking strategy evolves based on phase transitions. • HeteroRank ensures efficient critical transition drive. [ABSTRACT FROM AUTHOR] |
| Copyright of Physica A 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 |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 193957411 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: HeteroRank: Adaptive node ranking in heterogeneous networks incorporating a percolation-based switching mechanism. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Gao%2C+Mengxiao%22">Gao, Mengxiao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Song%2C+Yu-Rong%22">Song, Yu-Rong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> songyr@njupt.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Song%2C+Bo%22">Song, Bo</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Physica+A%22">Physica A</searchLink>. Aug2026, Vol. 695, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22First-order+phase+transitions%22">First-order phase transitions</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In heterogeneous networks with complex topologies, non-uniform connection patterns and diversity in node types limit the effectiveness of traditional centrality measures in node importance ranking. To address this issue, this paper proposes an adaptive node ranking method for heterogeneous networks incorporating a percolation switching mechanism, termed HeteroRank. The proposed method deeply couples node ranking with the network dismantling process by constructing a collaborative dismantling index to evaluate the role of nodes in maintaining global structural stability. By analyzing the topological differences between residual networks under varying removal proportions, the percolation threshold of network collapse is precisely identified. This threshold not only characterizes the critical phase transition point of the network topology but also serves as an adaptive switching mechanism, driving the dynamic evolution of the ranking strategy from global structural dismantling to local dominance evaluation. Extensive experiments demonstrate that this adaptive framework, which dynamically adjusts strategies based on topological phase transitions, significantly outperforms various baseline methods in terms of network dismantling efficiency, connectivity destruction capability, and suppression of spreading processes. These results validate the effectiveness and robustness of HeteroRank in identifying key nodes and implementing targeted interventions in heterogeneous networks. • Adaptive ranking coupled with network dismantling. • Collaborative score evaluates structural stability roles. • Percolation Threshold identifies critical topological transitions. • Ranking strategy evolves based on phase transitions. • HeteroRank ensures efficient critical transition drive. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Physica A 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=193957411 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.physa.2026.131644 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: First-order phase transitions Type: general Titles: – TitleFull: HeteroRank: Adaptive node ranking in heterogeneous networks incorporating a percolation-based switching mechanism. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Gao, Mengxiao – PersonEntity: Name: NameFull: Song, Yu-Rong – PersonEntity: Name: NameFull: Song, Bo IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 03784371 Numbering: – Type: volume Value: 695 Titles: – TitleFull: Physica A Type: main |
| ResultId | 1 |
