Characterizing the Interactions Between Classical and Community-aware Centrality Measures in Complex Networks

TL;DR

This paper investigates how classical and community-aware centrality measures relate in complex networks, revealing that community structure strength influences their effectiveness and interactions across various network types.

Contribution

It provides an extensive analysis of the relationship between classical and community-aware centrality measures using both artificial and real-world networks.

Findings

Community-aware measures are more suitable for networks with strong community structures.

Variations in degree and community size distributions do not significantly affect the results.

Network transitivity and community structure strength are key factors influencing measure interactions.

Abstract

Identifying vital nodes in networks exhibiting a community structure is a fundamental issue. Indeed, community structure is one of the main properties of real-world networks. Recent works have shown that community-aware centrality measures compare favorably with classical measures agnostic about this ubiquitous property. Nonetheless, there is no clear consensus about how they relate and in which situation it is better to use a classical or a community-aware centrality measure. To this end, in this paper, we perform an extensive investigation to get a better understanding of the relationship between classical and community-aware centrality measures reported in the literature. Experiments use artificial networks with controlled community structure properties and a large sample of real-world networks originating from various domains. Results indicate that the stronger the community structure, the more appropriate the community-aware centrality measures. Furthermore, variations of the degree and community size distribution parameters do not affect the results. Finally, network transitivity and community structure strength are the most significant drivers controlling the interactions between classical and community-aware centrality measures.