A novel voting measure for identifying influential nodes in complex networks based on local structure
Abstract Identifying influential nodes in real networks is significant in studying and analyzing the structural as well as functional aspects of networks. VoteRank is a simple and effective algorithm to identify high-spreading nodes. The accuracy and monotonicity of the VoteRank algorithm are poor a...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-85332-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841544765008510976 |
|---|---|
| author | Haoyang Li Xing Wang You Chen Siyi Cheng Dejiang Lu |
| author_facet | Haoyang Li Xing Wang You Chen Siyi Cheng Dejiang Lu |
| author_sort | Haoyang Li |
| collection | DOAJ |
| description | Abstract Identifying influential nodes in real networks is significant in studying and analyzing the structural as well as functional aspects of networks. VoteRank is a simple and effective algorithm to identify high-spreading nodes. The accuracy and monotonicity of the VoteRank algorithm are poor as the network topology fails to be taken into account.Given the nodes’ attributes and neighborhood structure, this paper put forward an algorithm based on the Edge Weighted VoteRank (EWV) for identifying influential nodes in the network. The proposed algorithm draws inspiration from human voting behavior and expresses the attractiveness of nodes to their first-order neighborhood using the weights of connecting edges. Similarity between nodes is introduced into the voting process, further enhancing the accuracy of the method. Additionally, this EWV algorithm addresses the problem of influential node clustering by reducing the voting ability of nodes in the second-order neighborhood of the most influential nodes. The validity of the presented algorithm is verified through experiments conducted on 12 different real networks of various sizes and structures, directly comparing it with 7 competing algorithms.Empirical results indicate a superiority of the presented algorithm over the remaining seven competing algorithms with respect to node differentiation ability, effectiveness, and ranked list accuracy. |
| format | Article |
| id | doaj-art-901af7a5b55b4d9689adc2602d2450f2 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-901af7a5b55b4d9689adc2602d2450f22025-01-12T12:20:37ZengNature PortfolioScientific Reports2045-23222025-01-0115112010.1038/s41598-025-85332-4A novel voting measure for identifying influential nodes in complex networks based on local structureHaoyang Li0Xing Wang1You Chen2Siyi Cheng3Dejiang Lu4Air Force Engineering UniversityAir Force Engineering UniversityAir Force Engineering UniversityAir Force Engineering UniversityAir Force Engineering UniversityAbstract Identifying influential nodes in real networks is significant in studying and analyzing the structural as well as functional aspects of networks. VoteRank is a simple and effective algorithm to identify high-spreading nodes. The accuracy and monotonicity of the VoteRank algorithm are poor as the network topology fails to be taken into account.Given the nodes’ attributes and neighborhood structure, this paper put forward an algorithm based on the Edge Weighted VoteRank (EWV) for identifying influential nodes in the network. The proposed algorithm draws inspiration from human voting behavior and expresses the attractiveness of nodes to their first-order neighborhood using the weights of connecting edges. Similarity between nodes is introduced into the voting process, further enhancing the accuracy of the method. Additionally, this EWV algorithm addresses the problem of influential node clustering by reducing the voting ability of nodes in the second-order neighborhood of the most influential nodes. The validity of the presented algorithm is verified through experiments conducted on 12 different real networks of various sizes and structures, directly comparing it with 7 competing algorithms.Empirical results indicate a superiority of the presented algorithm over the remaining seven competing algorithms with respect to node differentiation ability, effectiveness, and ranked list accuracy.https://doi.org/10.1038/s41598-025-85332-4Complex networksInfluential nodesEdge Weighted VoteRankSI modelSIR model |
| spellingShingle | Haoyang Li Xing Wang You Chen Siyi Cheng Dejiang Lu A novel voting measure for identifying influential nodes in complex networks based on local structure Scientific Reports Complex networks Influential nodes Edge Weighted VoteRank SI model SIR model |
| title | A novel voting measure for identifying influential nodes in complex networks based on local structure |
| title_full | A novel voting measure for identifying influential nodes in complex networks based on local structure |
| title_fullStr | A novel voting measure for identifying influential nodes in complex networks based on local structure |
| title_full_unstemmed | A novel voting measure for identifying influential nodes in complex networks based on local structure |
| title_short | A novel voting measure for identifying influential nodes in complex networks based on local structure |
| title_sort | novel voting measure for identifying influential nodes in complex networks based on local structure |
| topic | Complex networks Influential nodes Edge Weighted VoteRank SI model SIR model |
| url | https://doi.org/10.1038/s41598-025-85332-4 |
| work_keys_str_mv | AT haoyangli anovelvotingmeasureforidentifyinginfluentialnodesincomplexnetworksbasedonlocalstructure AT xingwang anovelvotingmeasureforidentifyinginfluentialnodesincomplexnetworksbasedonlocalstructure AT youchen anovelvotingmeasureforidentifyinginfluentialnodesincomplexnetworksbasedonlocalstructure AT siyicheng anovelvotingmeasureforidentifyinginfluentialnodesincomplexnetworksbasedonlocalstructure AT dejianglu anovelvotingmeasureforidentifyinginfluentialnodesincomplexnetworksbasedonlocalstructure AT haoyangli novelvotingmeasureforidentifyinginfluentialnodesincomplexnetworksbasedonlocalstructure AT xingwang novelvotingmeasureforidentifyinginfluentialnodesincomplexnetworksbasedonlocalstructure AT youchen novelvotingmeasureforidentifyinginfluentialnodesincomplexnetworksbasedonlocalstructure AT siyicheng novelvotingmeasureforidentifyinginfluentialnodesincomplexnetworksbasedonlocalstructure AT dejianglu novelvotingmeasureforidentifyinginfluentialnodesincomplexnetworksbasedonlocalstructure |