A network is a fundamental entity observed everywhere in nature. We observe a wide variety of networks around us, such as computer networks, social networks, communication networks, transport (rail and road) networks, financial networks, protein networks, power networks, and so on. Often, we also hear that for success in life, you should have a good network of people in your circle. In other words, you should have good networking skills. One might ask the following question: Does the formation of these networks have some scientific principles? If we understand them, then we can utilize them to our benefit. For example, if you want to capture a terrorist, you might have to understand the properties of a terrorist network; if you want to understand a financial problem, you need to understand financial networks. In the quest for understanding these networks, many researchers modeled networks as a graph theory problem. Most of these models are based on studying a specific network. However, in reality, networks are quite dynamic; often, one network interacts with other networks. Thus, these early models based on random graph theory, such as the model of Erdős and Rényi, were not adequate to understand the behavior of real networks. In the last decade, a new paradigm of network science has emerged to understand the networks of networks (NoN) based on a statistical physics perspective.

This edited book on NoN is a state-of-the-art reference book in the area. It tries to cover most of the important modeling aspects of NoN, and provides the mathematics of NoN to understand its dynamics. The book is divided into three parts. Chapters 1 to 5 of Part 1 cover theoretical approaches; chapters 6 to 10 of Part 2 cover applications; and chapters 11 to 15 cover phenomenological models. As most of the book’s authors are from diverse backgrounds, it was a difficult task for the editors to bring them to the same table. However, the book is able to do that by keeping readers motivated and by providing connections between different chapters. By considering different examples of complex networks, such as electrical networks, financial networks, and so on, the book is able to illustrate difficulties in understanding these networks. The reader gets an overview of the methodologies and different modeling approaches needed for studying the connections between independent networks.

Despite the diversity of topics chosen, the book is able to provide a comprehensive view of this quickly emerging area. Thus, I recommend it to readers from different backgrounds such as mathematics, computer science, and physics. The title of the book is quite catchy, which may motivate readers to explore this newly emerging area of research that has deep applications.