04266nam a22004695i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050000900153072001700162072002300179072001600202100008000218245021500298250001800513264006900531300006700600336002600667337002600693338003600719347002400755490004600779505037400825520211401199650001903313650001903332650002603351650002103377650002703398650003703425700007403462710003403536773002603570776003603596776003603632776003603668830004603704856004603750978-3-031-78058-5DE-He21320251030114942.0cr nn 008mamaa250218s2024    sz |    s    |||| 0|eng d  a97830317805857 a10.1007/978-3-031-78058-52doi 4aQ295 7aGPFC2bicssc 7aSCI0640002bisacsh 7aGPFC2thema1 aMakse, Hernán A.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut14aThe Science of Influencers and Superspreadersh[electronic resource] :bUsing Networks and Artificial Intelligence to Understand Fake News, Pandemics, Markets, and the Brain /cby Hernán A. Makse, Marta Zava.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXIX, 447 p. 119 illus., 101 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aUnderstanding Complex Systems,x1860-08400 aMathematical theories of influencers in complex networks -- Social media influencers and politics -- Influencers for marketing -- Influencers as superspreaders of disease -- Genetic influencers in gene regulatory networks -- Neural influencers in the brain -- Keystone species are influencers in ecosystems -- Networks and Artificial Intelligence in Finance -- Outlook.  aThis book explores the identification of influencers in complex networks, bridging theoretical approaches with practical applications across diverse fields. It examines interdisciplinary complex systems, including online social media, biological networks, brain networks, socioeconomic and financial systems, and ecosystems. The research presented aims to benefit scientists in relevant areas and inspire new scientific inquiries, potentially advancing the field of influencer identification. In this context, 'influencer' serves as an umbrella term for essential, core, or central nodes within any complex network. The book investigates various manifestations of influencers, such as key figures in social media, critical nodes in genetic and brain networks, keystone species in ecosystems, systemically important banks in financial markets, and disease superspreaders. These diverse scenarios are approached by mapping the influencer identification problem to challenges in physics or computer science. The book caters to readers at three distinct levels: 1. Those seeking mathematically rigorous theories of influencers will find Chapter 2 particularly valuable, as it delves into the mathematical foundations of influencer identification algorithms. Subsequent chapters explore the application of these theories across various disciplines. 2. Data scientists interested in implementing these algorithms in their research and practical work will find relevant information throughout the book. 3. Professionals in finance, marketing, politics, and social media, as well as readers curious about the intersection of big data, influencers, and AI, will gain insights into how these tools can enhance decision-making processes. These readers are encouraged to focus on the introduction and chapters most relevant to their fields, while briefly reviewing the more technical sections. By offering this multi-layered approach, the book aims to provide a comprehensive understanding of influencer identification in complex networks, from theoretical foundations to real-world applications across various domains. . 0aSystem theory. 0aDigital media. 0aMathematical physics.14aComplex Systems.24aDigital and New Media.24aMathematical Methods in Physics.1 aZava, Marta.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303178057808iPrinted edition:z978303178059208iPrinted edition:z9783031780608 0aUnderstanding Complex Systems,x1860-084040uhttps://doi.org/10.1007/978-3-031-78058-5