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- W4310471808 abstract "In large-scale social networks, rich-club phenomenon is prominent. The important nodes selected by the existing algorithms are often too clustered, and the overlapping influence between nodes leads to a poor effect of maximizing the comprehensive influence of the node group. To solve this problem, an important node group mining algorithm based on information entropy and overlap coefficient is designed inspired by iterative optimization ideas. The algorithm consists of two stages: importance initialization stage and importance adaptive iterative update stage. In the initialization stage, the information entropy is used to measure the amount of information of nodes, evaluate the local importance and global importance of nodes, and improve the accuracy of the algorithm. In the iterative update stage, the overlap coefficient is used to evaluate the influence of the currently selected most important node on its surrounding neighbors, and adaptively weaken its local importance. Through the simulation experiment of the SIR propagation model, on the four real network data, the average distance between the nodes selected by the algorithm is larger and the distribution is wider, which reduces the overlap of influence and obtains greater influence." @default.
- W4310471808 created "2022-12-10" @default.
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- W4310471808 date "2022-11-30" @default.
- W4310471808 modified "2023-10-18" @default.
- W4310471808 title "Important node group mining algorithm based on information entropy and overlap coefficient" @default.
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- W4310471808 doi "https://doi.org/10.1117/12.2659660" @default.
- W4310471808 hasPublicationYear "2022" @default.
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