2018/06/30 by Riccardo Marcaccioli, Giacomo Livan
Physics and Astronomy · #Complex Network Analysis Techniques #Complex network #Filter (signal processing) #Null (SQL) #Null hypothesis #Null model #Opinion Dynamics and Social Influence #Statistical Mechanics and Entropy #Statistical hypothesis testing #Statistical model #cond-mat.dis-nn #physics.data-an #physics.soc-ph
paper · pdf · doi:10.1038/s41467-019-08667-3
published as Nature Communications, 10, Article number: 745 (2019) · 24 pages, 14 figures, 2 tables
openalex created_date 2018/07/10 · openalex publication_date 2019/02/14 · arxiv created 2019/02/18 · arxiv updated 2019/02/19 · openalex updated_date 2026/08/05
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.