000 04625nam a22005535i 4500
001 978-3-031-78058-5
003 DE-He213
005 20251030114942.0
007 cr nn 008mamaa
008 250218s2024 sz | s |||| 0|eng d
020 _a9783031780585
_9978-3-031-78058-5
024 7 _a10.1007/978-3-031-78058-5
_2doi
050 4 _aQ295
072 7 _aGPFC
_2bicssc
072 7 _aSCI064000
_2bisacsh
072 7 _aGPFC
_2thema
100 1 _aMakse, Hernán A.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_990810
245 1 4 _aThe Science of Influencers and Superspreaders
_h[electronic resource] :
_bUsing Networks and Artificial Intelligence to Understand Fake News, Pandemics, Markets, and the Brain /
_cby Hernán A. Makse, Marta Zava.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXIX, 447 p. 119 illus., 101 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aUnderstanding Complex Systems,
_x1860-0840
505 0 _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.
520 _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. .
650 0 _aSystem theory.
_966519
650 0 _aDigital media.
_959274
650 0 _aMathematical physics.
650 1 4 _aComplex Systems.
_966520
650 2 4 _aDigital and New Media.
_965945
650 2 4 _aMathematical Methods in Physics.
_970877
700 1 _aZava, Marta.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_990811
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031780578
776 0 8 _iPrinted edition:
_z9783031780592
776 0 8 _iPrinted edition:
_z9783031780608
830 0 _aUnderstanding Complex Systems,
_x1860-0840
_967707
856 4 0 _uhttps://doi.org/10.1007/978-3-031-78058-5
912 _aZDB-2-PHA
912 _aZDB-2-SXP
999 _c66714
_d66714