A Centrality Measure for Cycles and Subgraphs II

TL;DR

This paper introduces a new group centrality measure in networks that extends eigenvector centrality, quantifies network flow interception, and effectively distinguishes protein complexes in biological networks.

Contribution

It provides a rigorous mathematical foundation for the group centrality measure, extending eigenvector centrality to groups and demonstrating its application to real-world networks.

Findings

The new centrality measure is between 0 and 1.

It generalizes eigenvector centrality to groups of nodes.

It effectively identifies protein complexes in biological networks.

Abstract

In a recent work we introduced a measure of importance for groups of vertices in a complex network. This centrality for groups is always between 0 and 1 and induces the eigenvector centrality over vertices. Furthermore, its value over any group is the fraction of all network flows intercepted by this group. Here we provide the rigorous mathematical constructions underpinning these results via a semi-commutative extension of a number theoretic sieve. We then established further relations between the eigenvector centrality and the centrality proposed here, showing that the latter is a proper extension of the former to groups of nodes. We finish by comparing the centrality proposed here with the notion of group-centrality introduced by Everett and Borgatti on two real-world networks: the Wolfe's dataset and the protein-protein interaction network of the yeast \textit{Saccharomyces cerevisiae}. In this latter case, we demonstrate that the centrality is able to distinguish protein complexes.