Mangold Lena, Roth Camille
Centre d'Analyse et de Mathématique Sociales (CNRS/EHESS), 54 Bd Raspail, 75006 Paris, France.
Computational Social Science Team, Centre Marc Bloch (CNRS/MEAE), Friedrichstr. 191, 10117 Berlin, Germany.
Commun Phys. 2024;7(1):331. doi: 10.1038/s42005-024-01819-y. Epub 2024 Oct 11.
Network analysis is often enriched by including an examination of node metadata. In the context of understanding the mesoscale of networks it is often assumed that node groups based on metadata and node groups based on connectivity patterns are intrinsically linked. This assumption is increasingly being challenged, whereby metadata might be entirely unrelated to structure or, similarly, multiple sets of metadata might be relevant to the structure of a network in different ways. We propose the metablox tool to quantify the relationship between a network's node metadata and its mesoscale structure, measuring the strength of the relationship and the type of structural arrangement exhibited by the metadata. We show on a number of synthetic and empirical networks that our tool distinguishes relevant metadata and allows for this in a comparative setting, demonstrating that it can be used as part of systematic meta analyses for the comparison of networks from different domains.
网络分析通常通过对节点元数据的检查来丰富。在理解网络中尺度的背景下,人们常常假设基于元数据的节点组和基于连接模式的节点组存在内在联系。这一假设正日益受到挑战,因为元数据可能与结构完全无关,或者类似地,多组元数据可能以不同方式与网络结构相关。我们提出了元块工具来量化网络节点元数据与其中尺度结构之间的关系,测量这种关系的强度以及元数据所展现的结构排列类型。我们在一些合成网络和实证网络上表明,我们的工具能够区分相关元数据,并在比较环境中做到这一点,证明它可作为系统元分析的一部分,用于比较来自不同领域的网络。