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• W2893872038 abstract "Abstract Identifying vital nodes in complex networks is a critical problem in the field of network theory. To this end, the Collective Influence (CI) algorithm has been introduced and shows high efficiency and scalability in searching for the influential nodes in the optimal percolation model. However, the crucial part of the CI algorithm, reinsertion, has not been significantly investigated or improved upon. In this paper, the author improves the CI algorithm and proposes a new algorithm called Collective-Influence-Disjoint-Set-Reinsertion ( CI DR ) based on disjoint-set reinsertion. Experimental results on 8 datasets with scales of a million nodes and 4 random graph networks demonstrate that the proposed CI DR algorithm outperforms other algorithms, including Betweenness centrality, Closeness centrality, PageRank centrality, Degree centrality (HDA), Eigenvector centrality, Nonbacktracking centrality and Collective Influence with original reinsertion, in terms of the Robustness metric. Moreover, CI DR is applied to an international competition on optimal percolation and ultimately ranks in 7th place." @default.
• W2893872038 created "2018-10-05" @default.
• W2893872038 creator A5060969098 @default.
• W2893872038 date "2018-09-28" @default.
• W2893872038 modified "2023-10-12" @default.
• W2893872038 title "Improved collective influence of finding most influential nodes based on disjoint-set reinsertion" @default.
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• W2893872038 doi "https://doi.org/10.1038/s41598-018-32874-5" @default.
• W2893872038 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6162239" @default.
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