论文标题
比较在线社交网络中的社区意识中心度措施
Comparing Community-aware Centrality Measures in Online Social Networks
论文作者
论文摘要
识别关键节点对于加速或阻碍网络中的动态扩展至关重要。通过利用网络的社区结构来解决这个问题，可以衡量社区意识的中心性。尽管设计新的社区意识中心度度量的趋势越来越大，但对拟议措施的有效性没有系统的研究。这项研究使用现实世界中的在线社交网络上的易感性感染重新授予的（SIR）模型对著名的社区感知中心度度量进行了广泛的比较评估。总体而言，结果表明，具有社区和基于社区的中心度度量的K壳是在单一宣传问题下识别有影响力的节点最准确的。此外，流行性传播率不会显着影响社区意识的中心度度量的行为。
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.
