Abstract
In view of the high time complexity of the current overlapping community discovery algorithm and the low stability, an overlapping community discovery algorithm OCDITN based on three-level neighbor influence is proposed. The algorithm uses three-level neighbor node influence measurement method TIM (Three-level Influence Measurement) to calculate the node influence, and determines the order of selecting and updating nodes according to the node influence; the similarity between the nodes is determined by the update sequence of neighbor node labels, and finally the label membership of each node is calculated to discover the overlapping communities. The experiment is performed based on the artificial simulation network data set and the real world network data set. Compared with the SLPA, LPANNI, and COPRA algorithms, the performance of this algorithm is improved by 7% and 12% on the two evaluation standards EQ and Qvo respectively.
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Acknowledgment
This study was funded by Guangdong Natural Science Fund Project (2021A1515011243), Guangzhou Science and Technology Plan Project (201902020016), Yunfu Science and Technology Plan Project S2021010104 and Guangdong Science and Technology Plan Project (2019B010139001, 2021B1212100004).
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Chen, S., Huang, G., Lin, S., Jiang, W., Zhao, Z. (2023). Overlapping Community Discovery Algorithm Based on Three-Level Neighbor Node Influence. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13656. Springer, Cham. https://doi.org/10.1007/978-3-031-20099-1_28
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