Comparing Community-Aware Centrality Measures in Online Social Networks

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原文链接

NETL
Springer Nature

外文摘要：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。

外文关键词：

Complex networksCentralityInfluential nodesCommunity structureSIR model

作者：

Stephany Rajeh、Marinette Savonnet、Eric Leclercq、Hocine Cherifi

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作者单位：

Laboratoire d'Informatique de Bourgogne, University of Burgundy, Dijon, France

会议名称：

International conference on computational data and social networks

会议母体文献：

Computational data and social networks

页码：

279-290

出版时间：

2021

DOI：

10.1007/978-3-030-91434-9_25