Measurement of Node Influence Based on Three-level Neighbor in Complex Networks

Shuxin YANG; Wen LIANG; Kaili ZHU

doi:10.11999/JEIT190440

Volume 42 Issue 5

Jun. 2020

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Shuxin YANG, Wen LIANG, Kaili ZHU. Measurement of Node Influence Based on Three-level Neighbor in Complex Networks[J]. Journal of Electronics & Information Technology, 2020, 42(5): 1140-1148. doi: 10.11999/JEIT190440

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Shuxin YANG, Wen LIANG, Kaili ZHU. Measurement of Node Influence Based on Three-level Neighbor in Complex Networks[J]. Journal of Electronics & Information Technology, 2020, 42(5): 1140-1148. doi: 10.11999/JEIT190440

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Measurement of Node Influence Based on Three-level Neighbor in Complex Networks

doi: 10.11999/JEIT190440

Faculty of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China

Funds: The National Natural Science Foundation of China (61662028), The Scientific Technology Research Foundation of the Education Department of Jiangxi Province (GJJ170518), The Special Foundation of Postgraduate Innovation of Jiangxi province (YC2018-S331)

Received Date: 2019-06-17
Rev Recd Date: 2020-02-02

Available Online: 2020-02-20

Publish Date: 2020-06-04

Abstract

Abstract

There are some limitations in the existing metric methods for measuring node influence. A measurement method of node influence with three-level neighbors is proposed, which is based on the principle of three-degree influence, and considering the appropriate level of local measurement and the scalability of the large-scale network. Firstly, the neighbors with propagation attenuation characteristics in the second and third level of a node are regarded as a whole, which is used to measure the influence of the node. Then, an algorithm for measure called Three-level Influence Measurement (TIM) is proposed. Finally, in order to validate the effectiveness of the algorithm, the experiments on three datasets are conducted by using susceptible-infected-recovered model and independent cascade model. The experimental results show that the proposed algorithm is superior in consistency of influence, discrimination, sorting performance and other evaluation indexes. Furthermore, the TIM is applied to effectively solve the problem of maximizing influence.
- Complex networks,
- Influence of nodes,
- Influence maximization

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References(19)

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