04625nam a22005535i 4500 978-3-031-78058-5 DE-He213 20251030114942.0 cr nn 008mamaa 250218s2024 sz | s |||| 0|eng d 9783031780585 978-3-031-78058-5 10.1007/978-3-031-78058-5 doi Q295 GPFC bicssc SCI064000 bisacsh GPFC thema Makse, Hernán A. author. aut http://id.loc.gov/vocabulary/relators/aut 90810 The Science of Influencers and Superspreaders [electronic resource] : Using Networks and Artificial Intelligence to Understand Fake News, Pandemics, Markets, and the Brain / by Hernán A. Makse, Marta Zava. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XIX, 447 p. 119 illus., 101 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Understanding Complex Systems, 1860-0840 Mathematical 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. This 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. . System theory. 66519 Digital media. 59274 Mathematical physics. Complex Systems. 66520 Digital and New Media. 65945 Mathematical Methods in Physics. 70877 Zava, Marta. author. aut http://id.loc.gov/vocabulary/relators/aut 90811 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031780578 Printed edition: 9783031780592 Printed edition: 9783031780608 Understanding Complex Systems, 1860-0840 67707 https://doi.org/10.1007/978-3-031-78058-5 ZDB-2-PHA ZDB-2-SXP 66714 66714