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- W4384338386 abstract "Centrality analysis is a crucial tool for understanding the role of nodes in a network, but it is unclear how different centrality measures provide much unique information. To improve the identification of influential nodes in a network, we propose a new method called Hybrid-GSM (H-GSM) that combines the K-shell decomposition approach and Degree Centrality. H-GSM characterizes the impact of nodes more precisely than the Global Structure Model (GSM), which cannot distinguish the importance of each node. We evaluate the performance of H-GSM using the SIR model to simulate the propagation process of six real-world networks. Our method outperforms other approaches regarding computational complexity, node discrimination, and accuracy. Our findings demonstrate the proposed H-GSM as an effective method for identifying influential nodes in complex networks." @default.
- W4384338386 created "2023-07-15" @default.
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- W4384338386 date "2023-07-14" @default.
- W4384338386 modified "2023-09-26" @default.
- W4384338386 title "Integrating local and global information to identify influential nodes in complex networks" @default.
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- W4384338386 doi "https://doi.org/10.1038/s41598-023-37570-7" @default.
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