Issue |
EPL
Volume 149, Number 2, January 2025
|
|
---|---|---|
Article Number | 21003 | |
Number of page(s) | 7 | |
Section | Statistical physics and networks | |
DOI | https://doi.org/10.1209/0295-5075/ada6f9 | |
Published online | 23 January 2025 |
LGP-DS: A novel algorithm for identifying influential nodes in complex networks based on multi-dimensional evidence fusion
1 School of Management, Shanghai University - Shanghai, China
2 School of Economics and Management, Jiangsu University of Science and Technology - Zhenjiang, China
Received: 15 August 2024
Accepted: 7 January 2025
Identifying influential nodes in complex networks is crucial in various application scenarios, such as blocking the spread of rumors, containing disease transmission and facilitating precise targeting of product advertisements. Existing researches have presented various centrality measures to identify influential nodes in the network, but most measures evaluate the importance of nodes from limited dimensions. To fill this gap, we propose a novel algorithm called Local-Global-Position based on the Dempster-Shafer evidence theory (LGP-DS) to solve the problem of identifying influential nodes. The proposed LGP-DS algorithm first calculates the information about propagation capability of nodes based on the global, local and position attributes, and thus obtains multiple evidence dimensions. Next, information entropy is employed to assess the contribution of different evidence dimensions, and the information is aggregated using Dempster-Shafer evidence theory, which facilitates the evaluation of nodal importance within a network. The effectiveness of the LGP-DS algorithm is validated by several simulated experiments on real-world networks. The results demonstrate that the proposed algorithm outperforms eight widely used algorithms in terms of discrimination power, top-10 nodes, and ranking accuracy.
© 2025 EPLA. All rights, including for text and data mining, AI training, and similar technologies, are reserved
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.