Version 1
: Received: 13 January 2023 / Approved: 23 January 2023 / Online: 23 January 2023 (01:45:54 CET)
How to cite:
Zhao, N.; Chen, L.; Li, J.; Long, Z.; Jing, M.; Wang, J. WEA: A Node Importance Algorithm in Weighted Networks. Preprints2023, 2023010385. https://doi.org/10.20944/preprints202301.0385.v1
Zhao, N.; Chen, L.; Li, J.; Long, Z.; Jing, M.; Wang, J. WEA: A Node Importance Algorithm in Weighted Networks. Preprints 2023, 2023010385. https://doi.org/10.20944/preprints202301.0385.v1
Zhao, N.; Chen, L.; Li, J.; Long, Z.; Jing, M.; Wang, J. WEA: A Node Importance Algorithm in Weighted Networks. Preprints2023, 2023010385. https://doi.org/10.20944/preprints202301.0385.v1
APA Style
Zhao, N., Chen, L., Li, J., Long, Z., Jing, M., & Wang, J. (2023). WEA: A Node Importance Algorithm in Weighted Networks. Preprints. https://doi.org/10.20944/preprints202301.0385.v1
Chicago/Turabian Style
Zhao, N., Ming Jing and Jian Wang. 2023 "WEA: A Node Importance Algorithm in Weighted Networks" Preprints. https://doi.org/10.20944/preprints202301.0385.v1
Abstract
The heterogeneous structure implies that a few nodes may be crucial in maintaining network structural and functional properties. Identifying these crucial nodes correctly and quickly is a primary issue as contemporarily may face the mushrooming of large-scale datasets. Besides, the ‘weight issue’ is always ignored in this field which edge weight may play a positive/negative role in contributing to the node importance in different weighted networks. This paper provides a novel algorithm, Weighted Expectation Algorithm (WEA), which aims to ensure accuracy and speed of computation by taking advantage of dynamic programming to better handle the task of large-scale networks. Additionally, the weight issue that edge weights may contribute differently is addressed by a simply quantitative definition. Two standard experiments show WEA can maintain the network structure in connectivity well (by the lowest average robustness 0.192) and identify the node importance better in the spreading function test of spreading dynamics (by the highest average Kendall’s tau-b 0.678). In addition, the time complexities of different algo-rithms are evaluated, and their time-consuming are tested, proving that WEA consumes a rela-tively short time.
Computer Science and Mathematics, Geometry and Topology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
