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• W3104660771 abstract "Abstract The increasing availability of data demands for techniques to filter information in large complex networks of interactions. A number of approaches have been proposed to extract network backbones by assessing the statistical significance of links against null hypotheses of random interaction. Yet, it is well known that the growth of most real-world networks is non-random, as past interactions between nodes typically increase the likelihood of further interaction. Here, we propose a filtering methodology inspired by the Pólya urn, a combinatorial model driven by a self-reinforcement mechanism, which relies on a family of null hypotheses that can be calibrated to assess which links are statistically significant with respect to a given network’s own heterogeneity. We provide a full characterization of the filter, and show that it selects links based on a non-trivial interplay between their local importance and the importance of the nodes they belong to." @default.
• W3104660771 created "2020-11-23" @default.
• W3104660771 creator A5035578279 @default.
• W3104660771 creator A5040844400 @default.
• W3104660771 date "2019-02-14" @default.
• W3104660771 modified "2023-10-17" @default.
• W3104660771 title "A Pólya urn approach to information filtering in complex networks" @default.
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• W3104660771 doi "https://doi.org/10.1038/s41467-019-08667-3" @default.
• W3104660771 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6375975" @default.
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• W3104660771 hasPublicationYear "2019" @default.
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