论文标题
复杂网络中的社区感知和经典核心度量如何相关？
How Correlated are Community-aware and Classical Centrality Measures in Complex Networks?
论文作者
论文摘要
与经典的集中度措施不同，最近开发的社区感知的中心度测度使用网络的社区结构来识别复杂网络中的有影响力的节点。本文研究了他们在源自各个领域的五十个现实世界网络上的关系。结果表明，经典和社区意识的中心度通常表现出低至中相关值。这些结果在整个网络之间是一致的。传递性和效率是最具影响力的宏观网络特征，推动了经典和社区意识的中心度度量之间的相关性变化。此外，混合参数，模块化和最大oDF是发挥最实质效应的主要介绍性拓扑特性。
Unlike classical centrality measures, recently developed community-aware centrality measures use a network's community structure to identify influential nodes in complex networks. This paper investigates their relationship on a set of fifty real-world networks originating from various domains. Results show that classical and community-aware centrality measures generally exhibit low to medium correlation values. These results are consistent across networks. Transitivity and efficiency are the most influential macroscopic network features driving the correlation variation between classical and community-aware centrality measures. Additionally, the mixing parameter, the modularity, and the Max-ODF are the main mesoscopic topological properties exerting the most substantial effect.
