A voting approach to uncover multiple influential spreaders on weighted networks
The identifcation of multiple spreaders on weighted complex networks is a crucial step towards effcient information diffusion, preventing epidemics spreading and etc. In this paper, we propose a novel approach WVoteRank to find multiple spreaders by extending VoteRank. VoteRank has limitations to se...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2019
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| Online Access: | https://eprints.nottingham.ac.uk/56158/ |
| _version_ | 1848799281865031680 |
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| author | Sun, Hong-liang Chen, Duan-bing He, Jia-lin Ch’ng, Eugene |
| author_facet | Sun, Hong-liang Chen, Duan-bing He, Jia-lin Ch’ng, Eugene |
| author_sort | Sun, Hong-liang |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | The identifcation of multiple spreaders on weighted complex networks is a crucial step towards effcient information diffusion, preventing epidemics spreading and etc. In this paper, we propose a novel approach WVoteRank to find multiple spreaders by extending VoteRank. VoteRank has limitations to select multiple spreaders on unweighted networks while various real networks are weighted networks such as trade networks, traffic flow networks and etc. Thus our approach WVoteRank is generalized to deal with both unweighted and weighted networks by considering both degree and weight in voting process. Experimental studies on LFR synthetic networks and real networks show that in the context of Susceptible-Infected-Recovered (SIR) propagation, WVoteRank outperforms existing states of arts methods such as weighted H-index, weighted K-shell, weighted degree centrality and weighted betweeness centrality on final affected scale. It obtains an improvement of final affected scale as much as 8:96%. Linear time complexity enables it to be applied on large networks effectively. |
| first_indexed | 2025-11-14T20:33:11Z |
| format | Article |
| id | nottingham-56158 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:33:11Z |
| publishDate | 2019 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-561582020-12-21T04:30:12Z https://eprints.nottingham.ac.uk/56158/ A voting approach to uncover multiple influential spreaders on weighted networks Sun, Hong-liang Chen, Duan-bing He, Jia-lin Ch’ng, Eugene The identifcation of multiple spreaders on weighted complex networks is a crucial step towards effcient information diffusion, preventing epidemics spreading and etc. In this paper, we propose a novel approach WVoteRank to find multiple spreaders by extending VoteRank. VoteRank has limitations to select multiple spreaders on unweighted networks while various real networks are weighted networks such as trade networks, traffic flow networks and etc. Thus our approach WVoteRank is generalized to deal with both unweighted and weighted networks by considering both degree and weight in voting process. Experimental studies on LFR synthetic networks and real networks show that in the context of Susceptible-Infected-Recovered (SIR) propagation, WVoteRank outperforms existing states of arts methods such as weighted H-index, weighted K-shell, weighted degree centrality and weighted betweeness centrality on final affected scale. It obtains an improvement of final affected scale as much as 8:96%. Linear time complexity enables it to be applied on large networks effectively. Elsevier 2019-04-01 Article PeerReviewed application/pdf en cc_by_nc_nd https://eprints.nottingham.ac.uk/56158/1/A%20Voting%20Approach%20to%20Uncover%20Multiple%20Influential%20Spreaders%20on%20Weighted%20Networks%20-%20Accepted%20version.pdf Sun, Hong-liang, Chen, Duan-bing, He, Jia-lin and Ch’ng, Eugene (2019) A voting approach to uncover multiple influential spreaders on weighted networks. Physica A: Statistical Mechanics and its Applications, 519 . pp. 303-312. ISSN 0378-4371 Multiple influential spreaders; Influence maximization; Weighted complex networks https://www.sciencedirect.com/science/article/pii/S0378437118315061?via%3Dihub doi:10.1016/j.physa.2018.12.001 doi:10.1016/j.physa.2018.12.001 |
| spellingShingle | Multiple influential spreaders; Influence maximization; Weighted complex networks Sun, Hong-liang Chen, Duan-bing He, Jia-lin Ch’ng, Eugene A voting approach to uncover multiple influential spreaders on weighted networks |
| title | A voting approach to uncover multiple influential spreaders on weighted networks |
| title_full | A voting approach to uncover multiple influential spreaders on weighted networks |
| title_fullStr | A voting approach to uncover multiple influential spreaders on weighted networks |
| title_full_unstemmed | A voting approach to uncover multiple influential spreaders on weighted networks |
| title_short | A voting approach to uncover multiple influential spreaders on weighted networks |
| title_sort | voting approach to uncover multiple influential spreaders on weighted networks |
| topic | Multiple influential spreaders; Influence maximization; Weighted complex networks |
| url | https://eprints.nottingham.ac.uk/56158/ https://eprints.nottingham.ac.uk/56158/ https://eprints.nottingham.ac.uk/56158/ |