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.
Hernan Makse currently serves as Distinguished Professor of Physics at the City College of New York, wherein he is responsible for the Complex Networks and Data Science Lab at the Levich Institute. He is also Member Affiliate at Memorial Sloan Kettering Cancer Center and CEO of Kcore Analytics, an AI company in New York City. He holds a Ph.D. degree in Physics from Boston University, he is Fellow of the American Physical Society and Member of the Brazilian Academy of Science. 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.
Marta Zava currently teaches and conducts her research at the Department of Finance in Bocconi University, Milan. Her PhD dissertation was nominated among the three best doctoral thesis in complex systems in France. She holds a PhD from Goethe University, Frankfurt am Main, Germany. Her research bridges the domains of venture capital, network science, financial markets and artificial intelligence. She regularly contributes to academic publications and books, presents at international conferences and engages in public outreach.
