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  Signal propagation in complex networks

Ji, P., Ye, J., Mu, Y., Lin, W., Tian, Y., Hens, C., Perc, M., Tang, Y., Sun, J., Kurths, J. (2023): Signal propagation in complex networks. - Physics Reports, 1017, 1-96.
https://doi.org/10.1016/j.physrep.2023.03.005

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 Creators:
Ji, Peng1, Author
Ye, Jiachen1, Author
Mu, Yu1, Author
Lin, Wei1, Author
Tian, Yang1, Author
Hens, Chittaranjan1, Author
Perc, Matjaž1, Author
Tang, Yang1, Author
Sun, Jie1, Author
Kurths, Jürgen2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 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.

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Language(s): eng - English
 Dates: 2023-04-042023-05-18
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.physrep.2023.03.005
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Tipping Elements
Model / method: Game Theory
Model / method: Nonlinear Data Analysis
 Degree: -

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Title: Physics Reports
Source Genre: Journal, SCI, Scopus
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Publ. Info: -
Pages: - Volume / Issue: 1017 Sequence Number: - Start / End Page: 1 - 96 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/physics-reports
Publisher: Elsevier