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- W3036634556 endingPage "100052" @default.
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- W3036634556 abstract "Biological systems are composed of highly complex networks, and decoding the functional significance of individual network components is critical for understanding healthy and diseased states. Several algorithms have been designed to identify the most influential regulatory points within a network. However, current methods do not address all the topological dimensions of a network or correct for inherent positional biases, which limits their applicability. To overcome this computational deficit, we undertook a statistical assessment of 200 real-world and simulated networks to decipher associations between centrality measures and developed an algorithm termed Integrated Value of Influence (IVI), which integrates the most important and commonly used network centrality measures in an unbiased way. When compared against 12 other contemporary influential node identification methods on ten different networks, the IVI algorithm outperformed all other assessed methods. Using this versatile method, network researchers can now identify the most influential network nodes." @default.
- W3036634556 created "2020-06-25" @default.
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- W3036634556 date "2020-08-01" @default.
- W3036634556 modified "2023-10-18" @default.
- W3036634556 title "Integrated Value of Influence: An Integrative Method for the Identification of the Most Influential Nodes within Networks" @default.
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- W3036634556 doi "https://doi.org/10.1016/j.patter.2020.100052" @default.
- W3036634556 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7660386" @default.
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