The Science of Influencers and Superspreaders Using Network Theory and Artificial Intelligence

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.