Abstract

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.

Original languageEnglish
Pages (from-to)1-96
Number of pages96
JournalPhysics Reports
Volume1017
DOIs
StatePublished - 18 May 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 Elsevier B.V.

Funding

We would like to thank Mr. Subrata Ghosh, Mr. Arpit Kumar and Ms. Shitong Zhao for fruitful suggestions and discussion. P.J. is supported by STI2030-Major Projects ( 2021ZD0204500 ) and the NSFC ( 62076071 ). J.Y. is supported by the NSFC ( 12147101 ) and Shanghai Municipal Science and Technology Major Project ( 2018SHZDZX01 ). Y. M. is supported by STI2030-Major Projects ( 2021ZD0203700 , 2021ZD0204500 ). W.L. is supported by the NSFC (No. 11925103 ) and by the STCSM (Nos. 22JC1401402 , 2021SHZDZX0103 , and 2023ZKZD04 ). C.H. is supported by DST-INSPIRE Faculty Grant No. IFA17-PH193 . M.P. is supported by the Slovenian Research Agency (Javna agencija zaraziskovalno dejavnost RS) (Grant Nos. P1-0403 and J1-2457 ). Y.T. is supported by the NSFC ( 61988101 , 62293502 , 62233005 ) and Shanghai AI Laboratory.

FundersFunder number
Javna agencija zaraziskovalno dejavnost RS61988101, 62233005, 62293502, P1-0403, J1-2457
Shanghai AI Laboratory
Department of Science and Technology, Ministry of Science and Technology, IndiaIFA17-PH193
National Natural Science Foundation of China62076071, 12147101
Science and Technology Commission of Shanghai Municipality11925103, 2021SHZDZX0103, 2021ZD0203700, 2018SHZDZX01, 2023ZKZD04, 22JC1401402
Javna Agencija za Raziskovalno Dejavnost RS

    Keywords

    • Complex networks
    • Nonlinear dynamics
    • Signal propagation

    Fingerprint

    Dive into the research topics of 'Signal propagation in complex networks'. Together they form a unique fingerprint.

    Cite this