Omar Yamila M, Plapper Peter
Faculty of Science, Communication and Medicine, University of Luxembourg, L-1359 Luxembourg, Luxembourg.
Entropy (Basel). 2020 Dec 15;22(12):1417. doi: 10.3390/e22121417.
Information entropy metrics have been applied to a wide range of problems that were abstracted as complex networks. This growing body of research is scattered in multiple disciplines, which makes it difficult to identify available metrics and understand the context in which they are applicable. In this work, a narrative literature review of information entropy metrics for complex networks is conducted following the PRISMA guidelines. Existing entropy metrics are classified according to three different criteria: whether the metric provides a property of the graph or a graph component (such as the nodes), the chosen probability distribution, and the types of complex networks to which the metrics are applicable. Consequently, this work identifies the areas in need for further development aiming to guide future research efforts.
信息熵度量已应用于广泛的被抽象为复杂网络的问题。这一不断增长的研究成果分散在多个学科中，这使得识别可用的度量并理解它们适用的背景变得困难。在这项工作中，按照PRISMA指南对复杂网络的信息熵度量进行了叙述性文献综述。现有的熵度量根据三个不同的标准进行分类：该度量是提供图的属性还是图组件（如节点）的属性、所选的概率分布以及该度量适用的复杂网络类型。因此，这项工作确定了需要进一步发展的领域，旨在指导未来的研究工作。
