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The science of influencers and super...

Makse, Hernán A.

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The science of influencers and superspreaders = using networks and artificial intelligence to understand fake news, pandemics, markets, and the brain /

Record Type:	Electronic resources : Monograph/item
Title/Author:	The science of influencers and superspreaders/ by Hernán A. Makse, Marta Zava.
Reminder of title:	using networks and artificial intelligence to understand fake news, pandemics, markets, and the brain /
Author:	Makse, Hernán A.
other author:	Zava, Marta.
Published:	Cham :Springer Nature Switzerland : : 2024.,
Description:	xix, 447 p. :ill. (chiefly color), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	System analysis - Mathematical models. -
Online resource:	https://doi.org/10.1007/978-3-031-78058-5
ISBN:	9783031780585

The science of influencers and superspreaders = using networks and artificial intelligence to understand fake news, pandemics, markets, and the brain /
Makse, Hernán A.

The science of influencers and superspreadersusing networks and artificial intelligence to understand fake news, pandemics, markets, and the brain /[electronic resource] :by Hernán A. Makse, Marta Zava. - Cham :Springer Nature Switzerland :2024. - xix, 447 p. :ill. (chiefly color), digital ;24 cm. - Understanding complex systems,1860-0840. - Understanding complex systems..

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.

ISBN: 9783031780585

Standard No.: 10.1007/978-3-031-78058-5doiSubjects--Topical Terms:

587843
System analysis
--Mathematical models.

LC Class. No.: QA402

Dewey Class. No.: 003

The science of influencers and superspreaders = using networks and artificial intelligence to understand fake news, pandemics, markets, and the brain /
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520 $a 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.
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