A parametric approach to information filtering in complex networks: The P\'olya filter

TL;DR

This paper introduces the Pólya filter, a novel network backbone extraction method based on a parametric null hypothesis inspired by the Pólya urn model, which accounts for non-random growth in complex networks.

Contribution

It develops a flexible, analytically tractable filtering approach that adapts to network heterogeneity and generalizes the disparity filter as a special case.

Findings

The Pólya filter effectively identifies significant links in weighted networks.

It generalizes existing disparity filtering methods.

Application to real datasets demonstrates its practical utility.

Abstract

The ever increasing availability of data demands for techniques to extract relevant information from complex interacting systems, which can often be represented as weighted networks. In recent years, 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 highly non-random, as past interactions between nodes typically increase the likelihood of further interaction. Here, we propose a network filtering methodology based 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 design such family of null hypotheses by adapting the P\'olya urn, a simple one-parameter combinatorial model driven by a self-reinforcement mechanism, to a network setting. We provide a full analytical description of our filter, and show that it retains or discards links based on a non-trivial interplay between their own local importance and the importance of the nodes they belong to. We prove that the widely used disparity filter can be recovered as a large-strength approximation for a specific value of the P\'olya filter's parameter, and illustrate our findings by applying the filter to two large network datasets.