Comparing Community-aware Centrality Measures in Online Social Networks
arXiv:2202.00515 · doi:10.1007/978-3-030-91434-9_25
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.
arXiv commentAccepted in International Conference on Computational Data and Social Networks - CSoNet 2021: Computational Data and Social Networks
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