Directed cycles as higher-order units of information processing in complex networks
File(s)
Accepted version
Author(s)
Rajpal, Hardik
Expert, Paul
Vasiliauskaite, Vaiva
Type
Journal Article
Abstract
Directed cycles are fundamental motifs in natural, social and artificial networks, yet their distinct roles in processing information remain poorly understood, particularly as higher-order structures. Furthermore, it is unclear how the function of a cycle depends on the wider network in which it is embedded. Here we show, using information-theoretic measures, that network size, sparsity and directionality critically shape how directed cycles process information. In networks with no preferred direction, feedforward cycles enable greater information flow, while feedback cycles support stronger information integration. The relative orientation of a feedforward cycle, and the structural incoherence it induces, tunes its capacity to generate higher-order behaviour. Introducing feedback into otherwise feedforward architectures increases the diversity of network activity while modifying the computational behaviour. In a supervised learning task, feedforward networks train faster and tolerate random node ablations, whereas feedback makes them robust to input noise. These findings reveal directed cycles as tunable computational units whose behaviour is set by their structural context.
Date Acceptance
2026-08-05
Citation
Communications Physics
ISSN
2399-3650
Publisher
Nature Portfolio
Journal / Book Title
Communications Physics
Copyright Statement
Copyright This paper is embargoed until publication. Once published the Version of Record (VoR) will be available on immediate open access.
License URL
Publication Status
Accepted