Information filtering in complex weighted networks

TL;DR

This paper introduces the GloSS filter, a weight filtering method for complex weighted networks that preserves the network's multiscale structure while identifying statistically significant edges.

Contribution

The paper proposes a novel global null model-based weight filtering technique that maintains the network's topological and weight distribution features.

Findings

GloSS filter effectively identifies relevant connections in real networks.

The method preserves the multiscale structure of complex networks.

It accurately quantifies the significance of edge weights.

Abstract

Many systems in nature, society and technology can be described as networks, where the vertices are the system's elements and edges between vertices indicate the interactions between the corresponding elements. Edges may be weighted if the interaction strength is measurable. However, the full network information is often redundant because tools and techniques from network analysis do not work or become very inefficient if the network is too dense and some weights may just reflect measurement errors, and shall be discarded. Moreover, since weight distributions in many complex weighted networks are broad, most of the weight is concentrated among a small fraction of all edges. It is then crucial to properly detect relevant edges. Simple thresholding would leave only the largest weights, disrupting the multiscale structure of the system, which is at the basis of the structure of complex networks, and ought to be kept. In this paper we propose a weight filtering technique based on a global null model (GloSS filter), keeping both the weight distribution and the full topological structure of the network. The method correctly quantifies the statistical significance of weights assigned independently to the edges from a given distribution. Applications to real networks reveal that the GloSS filter is indeed able to identify relevantconnections between vertices.