文献类型：期刊文献

英文题名：Signal propagation in complex networks

作者：Ji, Peng[1,2,3];Ye, Jiachen[1,4];Mu, Yu[5];Lin, Wei[6,7,8,9,10];Tian, Yang[11,13];Hens, Chittaranjan[14];Perc, Matjaz[15,16,17,18,19];Tang, Yang[8,12,20];Sun, Jie[21];Kurths, Jurgen[6,7,22,23]

机构：[1]Fudan Univ, Inst Sci & Technol Brain Inspired Intelligence, Shanghai 200433, Peoples R China;[2]Minist Educ, Key Lab Computat Neurosci & Brain Inspired Intelli, Shanghai 200433, Peoples R China;[3]Fudan Univ, MOE Frontiers Ctr Brain Sci, Shanghai 200433, Peoples R China;[4]Fudan Univ, Inst Atmospher Sci, CMA FDU Joint Lab Marine Meteorol, Shanghai 200433, Peoples R China;[5]Chinese Acad Sci, Inst Neurosci, Ctr Excellence Brain Sci & Intelligence Technol, State Key Lab Neurosci, 320 Yue Yang Rd, Shanghai 200031, Peoples R China;[6]Fudan Univ, Res Inst Intelligent Complex Syst, Shanghai 200433, Peoples R China;[7]Fudan Univ, MOE Frontiers Ctr Brain Sci, Shanghai 200433, Peoples R China;[8]Shanghai Artificial Intelligence Lab, Shanghai 200232, Peoples R China;[9]Fudan Univ, Sch Math Sci, SCMS, Shanghai 200433, Peoples R China;[10]Fudan Univ, CCSB, Shanghai 200433, Peoples R China;[11]Tsinghua Univ, Dept Psychol, Beijing 100084, Peoples R China;[12]Tsinghua Univ, Tsinghua Lab Brain & Intelligence, Beijing 100084, Peoples R China;[13]Huawei Technol Co Ltd, Cent Res Inst, Lab Adv Comp & Storage, Lab 2012, Beijing 100084, Peoples R China;[14]Int Inst Informat Technol, Ctr Computat Nat Sci & Bioinformat, Hyderabad 500032, India;[15]Univ Maribor, Fac Nat Sci & Math, Korosska cesta 160, Maribor 2000, Slovenia;[16]China Med Univ, China Med Univ Hosp, Dept Med Res, Taichung, Taiwan;[17]Alma Mater Europaea, Slovenska ul 17, Maribor 2000, Slovenia;[18]Complex Sci Hub Vienna, Josefstadterstr 39, A-1080 Vienna, Austria;[19]Kyung Hee Univ, Dept Phys, 26 Kyungheedae Ro, Seoul, South Korea;[20]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[21]Huawei Technol Co Ltd, Cent Res Inst, Theory Lab, Labs 2012,Sha Tin, Hong Kong 999077, Peoples R China;[22]Potsdam Inst Climate Impact Res PIK, D-14473 Potsdam, Germany;[23]Humboldt Univ, Dept Phys, D-12489 Berlin, Germany

年份：2023

卷号：1017

起止页码：1

外文期刊名：PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001011397200001)】；

基金：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.

语种：英文

外文关键词：Signal propagation; Complex networks; Nonlinear dynamics

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, includ-ing 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.