Letter to Chinese Readers[*]

From social networks to neural networks in our brains, networks have been used to model various phenomena across multiple disciplines. The interdisciplinary field of network science has rapidly gained attention over the last two decades, offering unique insights into the analysis and modeling of complex systems that span all domains of knowledge, including physical, life, social, and applied sciences.

Despite being a field that has been around for more than two decades, network science has undergone significant transformations over the years. These transformations have been driven by cross-fertilization between various disciplines and applied mathematics. The field has evolved from being pioneered by social scientists and biologists to attracting visionary physicists with interdisciplinary mindsets, who have contributed immensely to building what is now known as network science. It has become a fundamental tool for analyzing complex systems that are characterized by multiple types of simultaneous interactions among units and interdependencies.

However, classical methods developed by network scientists are not enough to describe and account for the complexity of multilayer networks, which are characterized by simultaneous interactions among different layers. Despite numerous publications and some volumes dedicated to this novel framework, a comprehensive text on the data science of multilayer networks is still missing. To fill this gap, this book aims to provide practical recipes for the analysis and visualization of empirical multilayer networks in a wide range of applications, such as in urban transport, human mobility, computational social sciences, neuroscience, molecular medicine, and digital humanities.

As a complex systems scientist, I am convinced that methodologies developed to study complex systems are revolutionizing our approach to studying the physical world. This revolution is being embraced positively by several disciplines traditionally not related to physics, such as systems biology, social sciences, and emerging fields like network medicine, econophysics, and social physics. Even the most recent advances in computer science, such as artificial intelligence, are starting to benefit from standard concepts developed by physicists.

The book is aimed at the next generation of physicists, computational biologists and computer scientists willing to train in Complexity Science. As we live in the century of complexity, which requires both theoretical and computational skills, the book aims to provide readers with a basic theoretical foundation behind the computational tool that is used, as well as practical guidelines and examples to use them for analyzing the real world. It should be noted that the reader interested in a broader theoretical overview of multilayer network science cannot be satisfied only by this work, which should instead be considered as complementary to textbooks dedicated to that specific purpose.

This book complements the theoretical tools by describing a computational framework called muxViz, a set of visual tools based on a large library of functions written in R. MuxViz was developed for the analysis and visualization of multilayer systems and is a testament to the interdisciplinary mindset and efforts that have driven multilayer network science in the last decade. 

Manlio De Domenico, Ph.D.
Associate Professor of Applied Physics
Dept. of Physics & Astronomy "Galileo Galilei", University of Padua
Complex Multilayer Networks Lab

2023-05-01

[*] 受李杰博士邀请，Manlio De Domenico为中国读者撰写的关于本书多层网络内容概要。