ArticleScientific reports2018
Improved collective influence of finding most influential nodes based on disjoint-set reinsertion.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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3 citing papers in PubMed.
- Locating causal hubs of memory consolidation in spontaneous brain network in male mice.Nature communications · 2023Article
- Connectomic analysis of Alzheimer's disease using percolation theory.Network neuroscience (Cambridge, Mass.) · 2022Article
- Network analysis of population flow among major cities and its influence on COVID-19 transmission in China.Cities (London, England) · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
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
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