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• W4312407206 abstract "This paper presents a novel approach capable of finding influential nodes in controlling data diffusion, discouraging rumor, promotion and marketing inside social networks. Three gradient levels, include the algorithm (WRBC) weak relation-based centrality, provide three insights for influential within (a relationship between two nodes, the relationships of each node and whole network). We will prove correlation the new indicator with other standard indicators. Within the first phase, the algorithm was able to identify worthwhile and essential nodes that have a significant impact on the level control of the shortest individual paths, as well as for the second phase of the group of nodes that control short paths a specific node, and the third phase shows the nodes affecting the entire network. Finally, the algorithm creates secondary features that help understand data diffusion patterns within networks, which are the average of path’s lengths and the average of alternative paths for each node." @default.
• W4312407206 created "2023-01-04" @default.
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• W4312407206 date "2022-01-01" @default.
• W4312407206 modified "2023-10-15" @default.
• W4312407206 title "A weak relation-based approach to detect influential nodes in complex network" @default.
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• W4312407206 doi "https://doi.org/10.1063/5.0093387" @default.
• W4312407206 hasPublicationYear "2022" @default.
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