The Science of Influencers and Superspreaders Using Network Theory and Artificial Intelligence
Springer International Publishing (Verlag)
978-3-031-21115-7 (ISBN)
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This open access book investigates theoretical approaches to the problem of finding influencers in complex networks with emphasis on application works in a series of interdisciplinary complex systems covering online social media, biological networks, brain networks, socioeconomic and financial systems and ecosystems. These applications can benefit scientists working in relevant areas and may spark new scientific problems that in turn, stimulate the advance of research on influencer identification.
In this book, 'influencer' is used as an umbrella term that can describe essential, critical, core, or central nodes in any type of a complex network. Influencers in social media, essential nodes in genetic networks and the brain, ecosystems and financial markets, and superspreaders of disease are studied mapping to a physics or computer science problem as indicated.
This book is intended to inform readers at three different levels: First, those interested in the mathematically rigorous theories of influencers can concentrate in Chapter 2 where we explain the mathematics behind the algorithms to identify influencers. Readers can also focus on the subsequent chapters where we explain the applications of the theory of influencers to disciplines ranging from sociology, biology, and markets. The second level would be typical data scientists, who are interested in applying these algorithms in their research and day-to-day work. Third, this book is also intended to reach audiences in the financial, marketing, politics, and social media fields, and overall audience keen on learning how big data, influencers, and AI can contribute to a better decision-making process based on mathematically proven algorithms and advanced analytics in their field and business models.
lt;p>Hernan Makse currently serves as Distinguished Professor of Physics at the Physics Department of City College of New York, wherein he is responsible for the Complex Networks and Data Science Lab at the Levich Institute. He is also a Member Affiliate at Memorial Sloan Kettering Cancer Center and co-founder of Kcore Analytics, an AI company in New York City. He holds a Ph.D. degree in Physics from Boston University, and he is a fellow of the American Physical Society. His research focuses on the theoretical understanding of complex systems from a statistical physics viewpoint. He is working towards developing of new emergent laws for complex systems, ranging from brain networks to biological and social systems.
Soffia Alarcon currently works for Schneider Electric. She has a wealth of experience advising governments, corporate, and financial institutions on decarbonization pathways, corporate sustainability, carbon markets, sustainable finance, ESG, GHG accounting, target setting, Scope 3, and climate risks. She has worked for global organizations, including The Carbon Trust, World Resources Institute, and IHS Markit (now S&P Global). She is a Columbia University graduate, a LEAD fellow, and a Gamechanger by Bloomberg Businessweek. She was one of the international judges for the Million Cool Roofs Initiative. In 2022, she was appointed one of the 12 most influential women in sustainable finance and ESG in Latin America by PRI.
Mathematical Theories of Influencers in Complex Networks.-Social Media Influencers.- Influencers in Marketing.- How the Influencers theory can stop COVID-19?.- Essential Nodes for Integration in Brain Networks.- Influencers on ecosystems and Keystone Species.- Influencers on Financial networks and Markets.- How to Solve the World's Most Pressing Problems Using Big Data and AI?.- Conclusion.
| Erscheint lt. Verlag | 4.4.2024 |
|---|---|
| Reihe/Serie | Understanding Complex Systems |
| Zusatzinfo | XXIV, 479 p. 132 illus., 113 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Theorie / Studium |
| Naturwissenschaften ► Physik / Astronomie ► Theoretische Physik | |
| Sozialwissenschaften ► Kommunikation / Medien ► Medienwissenschaft | |
| Schlagworte | brain networks • complex biological networks • influencers in complex networks • influencers theory • Maximization of Influence Problem (MIP) • open access • Social Media Influencers |
| ISBN-10 | 3-031-21115-4 / 3031211154 |
| ISBN-13 | 978-3-031-21115-7 / 9783031211157 |
| Zustand | Neuware |
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