Comparative evaluation of community-aware centrality measures

TL;DR

This paper systematically evaluates seven community-aware centrality measures in real-world networks to determine their effectiveness in epidemic spreading scenarios, revealing insights into their correlations and optimal application strategies.

Contribution

It provides the first comprehensive comparison of community-aware centrality measures, guiding practical selection based on network structure and resource availability.

Findings

Low correlation among community-aware centrality measures.

Targeting distant hubs with Modularity Vitality is effective with ample resources.

Using bridges for diffusion is better with limited resources in structured networks.

Abstract

Influential nodes play a critical role in boosting or curbing spreading phenomena in complex networks. Numerous centrality measures have been proposed for identifying and ranking the nodes according to their importance. Classical centrality measures rely on various local or global properties of the nodes. They do not take into account the network community structure. Recently, a growing number of researches have shifted to community-aware centrality measures. Indeed, it is a ubiquitous feature in a vast majority of real-world networks. In the literature, the focus is on designing community-aware centrality measures. However, up to now, there is no systematic evaluation of their effectiveness. This study fills this gap. It allows answering which community-aware centrality measure should be used in practical situations. We investigate seven influential community-aware centrality measures in an epidemic spreading process scenario using the Susceptible-Infected-Recovered (SIR) model on a set of fifteen real-world networks. Results show that generally, the correlation between community-aware centrality measures is low. Furthermore, in a multiple-spreader problem, when resources are available, targeting distant hubs using Modularity Vitality is more effective. However, with limited resources, diffusion expands better through bridges, especially in networks with a medium or strong community structure.