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- W4295136277 abstract "The problem of influence maximization is a classic issue that has been well-studied in the field of network science, but most of existing researches are compromising among computational complexity or result accuracy. In this work, a three-stage iterative framework for influence maximization (TSIFIM) is presented to find a set of seed spreaders in complex networks. In TSIFIM, the initial candidate seeds are first selected by considering the global communicability of each node and its importance in their local network. Then, in addition to the candidate seeds, other remained nodes are assigned to the specific communities based on the proposed local resource allocation similarity index, and the core node in each community which satisfies the local influence threshold condition are selected as the supplementary candidate seeds. Furthermore, we employ an adaptive search strategy to find the optimal solution among these candidates. The proposed algorithm is compared with eight popular influence maximization algorithms on nine real-world networks to verify the performance. Experimental results show that TSIFIM has better performance in terms of influence spreading, sensitivity analysis, seed dispersion and statistical test. • A novel algorithm is proposed for solving influence maximization problem. • TSIFIM algorithm combines communicability network matrix and community structure. • Candidate seeds are determined by using local and global attributes. • An adaptive search strategy is designed to obtain optimal seed set solution. • TSIFIM is superior to eight contrast methods in nine datasets." @default.
- W4295136277 created "2022-09-11" @default.
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- W4295136277 date "2023-02-01" @default.
- W4295136277 modified "2023-09-26" @default.
- W4295136277 title "TSIFIM: A three-stage iterative framework for influence maximization in complex networks" @default.
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- W4295136277 doi "https://doi.org/10.1016/j.eswa.2022.118702" @default.
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