# Comparative evaluation of community-aware centrality measures

arXiv: https://arxiv.org/abs/2205.06995 · HTML: https://mlsanity.com/paper/2205.06995/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2022.

## Built on by (most cited first, 2 of 2)

- [Artificial Benchmark for Community Detection with Outliers (ABCD+o)](https://mlsanity.com/paper/2301.05749/) (2023)
- [LARAG: Link-Aware Retrieval Strategy for RAG Systems in Hyperlinked Technical Documentation](https://mlsanity.com/paper/2605.07517/) (2026)

## Abstract (arXiv)

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.