Organisation of signal flow in directed networks

TL;DR

This paper introduces a global network flow analysis method based on convergence degree and overlap, revealing how network structure influences signal propagation and causality, with applications to real-world and model networks.

Contribution

It defines convergence degree and overlap based on shortest paths, clarifies network causality, and analyzes real-world and model networks' flow properties, highlighting differences from traditional community structures.

Findings

Flow representation aligns with known node functions.

Real-world networks show random global connections but structured local patterns.

Small-world networks' flow properties are not reproducible by common algorithms.

Abstract

Confining an answer to the question whether and how the coherent operation of network elements is determined by the the network structure is the topic of our work. We map the structure of signal flow in directed networks by analysing the degree of edge convergence and the overlap between the in- and output sets of an edge. Definitions of convergence degree and overlap are based on the shortest paths, thus they encapsulate global network properties. Using the defining notions of convergence degree and overlapping set we clarify the meaning of network causality and demonstrate the crucial role of chordless circles. In real-world networks the flow representation distinguishes nodes according to their signal transmitting, processing and control properties. The analysis of real-world networks in terms of flow representation was in accordance with the known functional properties of the network nodes. It is shown that nodes with different signal processing, transmitting and control properties are randomly connected at the global scale, while local connectivity patterns depart from randomness. Grouping network nodes according to their signal flow properties was unrelated to the network's community structure. We present evidence that signal flow properties of small-world-like, real-world networks can not be reconstructed by algorithms used to generate small-world networks. Convergence degree values were calculated for regular oriented trees, and its probability density function for networks grown with the preferential attachment mechanism. For Erd\H{o}s-R\'enyi graphs we calculated both the probability density function of convergence degrees and of overlaps.