Signal propagation in complex networks

物理 网络拓扑 人工智能 复杂网络 非线性系统 封面(代数) 人工神经网络 信号(编程语言) 不断发展的网络 网络科学 信号处理 数据科学 机器学习 拓扑(电路) 电信 计算机网络 程序设计语言 计算机科学 机械工程 雷达 数学 量子力学 组合数学 万维网 工程类
作者
Peng Ji,Jiachen Ye,Yu Mu,Wei Lin,Yang Tian,Chittaranjan Hens,Matjaž Perc,Yang Tang,Jie Sun,Jürgen Kurths
出处
期刊:Physics Reports [Elsevier]
卷期号:1017: 1-96 被引量:113
标识
DOI:10.1016/j.physrep.2023.03.005
摘要

Signal propagation in complex networks drives epidemics, is responsible for information going viral, promotes trust and facilitates moral behavior in social groups, enables the development of misinformation detection algorithms, and it is the main pillar supporting the fascinating cognitive abilities of the brain, to name just some examples. The geometry of signal propagation is determined as much by the network topology as it is by the diverse forms of nonlinear interactions that may take place between the nodes. Advances are therefore often system dependent and have limited translational potential across domains. Given over two decades worth of research on the subject, the time is thus certainly ripe, indeed the need is urgent, for a comprehensive review of signal propagation in complex networks. We here first survey different models that determine the nature of interactions between the nodes, including epidemic models, Kuramoto models, diffusion models, cascading failure models, and models describing neuronal dynamics. Secondly, we cover different types of complex networks and their topologies, including temporal networks, multilayer networks, and neural networks. Next, we cover network time series analysis techniques that make use of signal propagation, including network correlation analysis, information transfer and nonlinear correlation tools, network reconstruction, source localization and link prediction, as well as approaches based on artificial intelligence. Lastly, we review applications in epidemiology, social dynamics, neuroscience, engineering, and robotics. Taken together, we thus provide the reader with an up-to-date review of the complexities associated with the network's role in propagating signals in the hope of better harnessing this to devise innovative applications across engineering, the social and natural sciences as well as to inspire future research.
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