Abstract
Identifying the most influential spreaders in complex networks is vital for optimally using the network structure and accelerating information diffusion. In most previous methods, the edges are treated equally and their potential importance is ignored. In this paper, a novel algorithm based on Two-Degree Centrality called TDC is proposed to identify influential spreaders. Firstly, the weight of edge is defined based on the power-law function of degree. Then, the node weight is calculated by the weight of its connected edges. Finally, the spreading influence of node is defined by considering the influence degree of the neighborhoods within 2 steps. In order to evaluate the performance of TDC, the Susceptible-Infected-Recovered (SIR) model is used to simulate the spreading process. Experiment results show that TDC can identify influential spreaders more effectively than the other comparative centrality algorithms.
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Acknowledgment
This work is funded by the Natural Science Foundation of Hebei Province of China under Grant No. F2022203089 and F2022203026, the Science and Technology Project of Hebei Education Department under Grant Nos. QN2021145 and BJK2022029, the National Natural Science Foundation of China under Grant No.61807028. The authors are grateful to valuable comments and suggestions of the reviewers.
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Wang, Q., Ren, J., Zhang, H., Wang, Y., Zhang, B. (2022). Identifying Influential Spreaders in Complex Networks Based on Degree Centrality. In: Zhao, X., Yang, S., Wang, X., Li, J. (eds) Web Information Systems and Applications. WISA 2022. Lecture Notes in Computer Science, vol 13579. Springer, Cham. https://doi.org/10.1007/978-3-031-20309-1_28
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