Impact of hierarchical modular structure on ranking of individual nodes in directed networks

TL;DR

This paper introduces an analytical method to estimate node centrality in directed modular networks by combining local and global properties, revealing hierarchical structures and improving ranking efficiency.

Contribution

It presents a novel approximation technique for node centrality that leverages modular structure, enabling efficient ranking in large or incomplete directed networks.

Findings

Centrality can be estimated using modular structure and node degrees.

The method links module importance to individual node rankings.

Hierarchical network structures are revealed through centrality analysis.

Abstract

Many systems, ranging from biological and engineering systems to social systems, can be modeled as directed networks, with links representing directed interaction between two nodes. To assess the importance of a node in a directed network, various centrality measures based on different criteria have been proposed. However, calculating the centrality of a node is often difficult because of the overwhelming size of the network or the incomplete information about the network. Thus, developing an approximation method for estimating centrality measures is needed. In this study, we focus on modular networks; many real-world networks are composed of modules, where connection is dense within a module and sparse across different modules. We show that ranking-type centrality measures including the PageRank can be efficiently estimated once the modular structure of a network is extracted. We develop an analytical method to evaluate the centrality of nodes by combining the local property (i.e., indegree and outdegree of nodes) and the global property (i.e., centrality of modules). The proposed method is corroborated with real data. Our results provide a linkage between the ranking-type centrality values of modules and those of individual nodes. They also reveal the hierarchical structure of networks in the sense of subordination (not nestedness) laid out by connectivity among modules of different relative importance. The present study raises a novel motive of identifying modules in networks.