LIB EA 7534, University of Burgundy, Dijon, France.
Sci Rep. 2021 May 12;11(1):10088. doi: 10.1038/s41598-021-89549-x.
Identifying vital nodes in networks exhibiting a community structure is a fundamental issue. Indeed, community structure is one of the main properties of real-world networks. Recent works have shown that community-aware centrality measures compare favorably with classical measures agnostic about this ubiquitous property. Nonetheless, there is no clear consensus about how they relate and in which situation it is better to use a classical or a community-aware centrality measure. To this end, in this paper, we perform an extensive investigation to get a better understanding of the relationship between classical and community-aware centrality measures reported in the literature. Experiments use artificial networks with controlled community structure properties and a large sample of real-world networks originating from various domains. Results indicate that the stronger the community structure, the more appropriate the community-aware centrality measures. Furthermore, variations of the degree and community size distribution parameters do not affect the results. Finally, network transitivity and community structure strength are the most significant drivers controlling the interactions between classical and community-aware centrality measures.
识别具有社区结构的网络中的重要节点是一个基本问题。事实上，社区结构是现实世界网络的主要特性之一。最近的研究表明，社区感知的中心性度量与不了解这种普遍存在的特性的经典度量相比具有优势。然而，关于它们之间的关系以及在何种情况下使用经典或社区感知的中心性度量更好，还没有明确的共识。为此，在本文中，我们进行了广泛的调查，以更好地理解文献中报告的经典和社区感知中心性度量之间的关系。实验使用具有受控社区结构特性的人工网络和来自不同领域的大量真实网络样本。结果表明，社区结构越强，社区感知的中心性度量就越合适。此外，度和社区大小分布参数的变化不会影响结果。最后，网络传递性和社区结构强度是控制经典和社区感知中心性度量之间相互作用的最重要驱动因素。
