Comparing Community-aware Centrality Measures in Online Social Networks

Abstract

Identifying key nodes is crucial for accelerating or impeding dynamic spreading in a network. Community-aware centrality measures tackle this problem by exploiting the community structure of a network. Although there is a growing trend to design new community-aware centrality measures, there is no systematic investigation of the proposed measures' effectiveness. This study performs an extensive comparative evaluation of prominent community-aware centrality measures using the Susceptible-Infected-Recovered (SIR) model on real-world online social networks. Overall, results show that K-shell with Community and Community-based Centrality measures are the most accurate in identifying influential nodes under a single-spreader problem. Additionally, the epidemic transmission rate doesn't significantly affect the behavior of the community-aware centrality measures.

Accepted in International Conference on Computational Data and Social Networks - CSoNet 2021: Computational Data and Social Networks

References in corpus (4)

• Maps of random walks on complex networks reveal community structure
• Vital nodes identification in complex networks
• Characterizing the Interactions Between Classical and Community-aware Centrality Measures in Complex Networks
• Identifying the Community Roles of Social Capitalists in the Twitter Network

Cited by in corpus (1)

• Comparative evaluation of community-aware centrality measures