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Multilayer networks

计算机科学 术语 相互依存的网络 构造(python库) 数据科学 相互依存 集合(抽象数据类型) 不断发展的网络 复杂网络 网络科学 理论计算机科学 人工智能 万维网 计算机网络 哲学 语言学 政治学 法学 程序设计语言
作者
Mikko Kivelä,Àlex Arenas,Marc Barthélemy,James P. Gleeson,Yamir Moreno,Mason A. Porter
出处
期刊:Journal of Complex Networks [Oxford University Press]
卷期号:2 (3): 203-271 被引量:2260
标识
DOI:10.1093/comnet/cnu016
摘要

In most natural and engineered systems, a set of entities interact with each other in complicated patterns that can encompass multiple types of relationships, change in time and include other types of complications. Such systems include multiple subsystems and layers of connectivity, and it is important to take such ‘multilayer’ features into account to try to improve our understanding of complex systems. Consequently, it is necessary to generalize ‘traditional’ network theory by developing (and validating) a framework and associated tools to study multilayer systems in a comprehensive fashion. The origins of such efforts date back several decades and arose in multiple disciplines, and now the study of multilayer networks has become one of the most important directions in network science. In this paper, we discuss the history of multilayer networks (and related concepts) and review the exploding body of work on such networks. To unify the disparate terminology in the large body of recent work, we discuss a general framework for multilayer networks, construct a dictionary of terminology to relate the numerous existing concepts to each other and provide a thorough discussion that compares, contrasts and translates between related notions such as multilayer networks, multiplex networks, interdependent networks, networks of networks and many others. We also survey and discuss existing data sets that can be represented as multilayer networks. We review attempts to generalize single-layer-network diagnostics to multilayer networks. We also discuss the rapidly expanding research on multilayer-network models and notions like community structure, connected components, tensor decompositions and various types of dynamical processes on multilayer networks. We conclude with a summary and an outlook.
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