A Polya urn approach to information filtering in complex networks.

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 characteriz

SUBMITTER: Marcaccioli R 

PROVIDER: S-EPMC6375975 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature