Abstract

When we represent real-world systems as networks, the directions of links often convey valuable information. Finding module structures that respect link directions is one of the most important tasks for analysing directed networks. Although many notions of a directed module have been proposed, no consensus has been reached. This lack of consensus results partly because there might exist distinct types of modules in a single directed network, whereas most previous studies focused on an independent criterion for modules. To address this issue, we propose a generic notion of the so-called truss structures in directed networks. Our definition of truss is able to extract two distinct types of trusses, named the cycle truss and the flow truss, from a unified framework. By applying the method for finding trusses to empirical networks obtained from a wide range of research fields, we find that most real networks contain both cycle and flow trusses. In addition, the abundance of (and the overlap between) the two types of trusses may be useful to characterize module structures in a wide variety of empirical networks. Our findings shed light on the importance of simultaneously considering different types of modules in directed networks.

Highlights

  • Analysis methods developed in network science provide us with useful tools for investigating and characterizing the kinds of network structures observed in real-world systems [1]

  • We use two networks obtained from different fields: the neural network of Caenorhabditis elegans (C. elegans) [22]

  • We found that the abundance of cycle and flow trusses helps us to classify empirical networks obtained from different fields

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Summary

Introduction

Analysis methods developed in network science provide us with useful tools for investigating and characterizing the kinds of network structures observed in real-world systems [1]. Standard techniques in network science include characterizing global properties of networks, measuring centralities of nodes and links, and classifying nodes into groups [2,3]. Finding relevant subgroups of nodes, often called communities or modules, is a fundamental problem. This problem is referred to as the community detection problem [4,5], which has been studied in different disciplines, including computer science, statistics and statistical physics.

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