Societies, economies, the brain, the body, and the Internet are complex systems. Complex systems are systems in which the whole is other than the sum of its parts. To understand the function of complex systems, one must study the functioning of individual parts in isolation but how these individual parts interact with each other. These interactions can be mathematically described by networks. In networks, nodes represent people, animals, businesses, locations, cells, and products, whereas a link represents the connection between those nodes, for example, social ties, historical events, proximity, similarity, or biological interactions. Networks are so flexible and general as a mathematical tool that they can be virtually applied to any problem. The fundamental use of the framework of networks is to map all those interactions, inter-dependencies, or relations between the parts of a complex system and find the function of network structures. Research on network science thus involves a methodological component, e.g. procedures for offline and online data collection, mathematical and computational methods to detect structural patterns, modelling of dynamic processes, and an applied component that uses those techniques to specific problems of societal importance, e.g. the spread of infectious diseases, online product/content recommendation, the emergence of consciousness, cascade failures on power-grids, or even to improve milk production by analysing social hierarchies of dairy cows living in a barn. Given the broad range of potential applications of network science, it is natural that the same methods can be used in different disciplines. The same algorithms used to make online recommendations can be used, for example, to identify synchronised parts of the brain or financial institutions. It is also intriguing that the same structural patterns are often detected in seemingly completely different natural and artificial systems. The beauty of network science is precisely the ability to understand how simple, local rules of behaviour, no matter what is being studied, lead to the emergence of macroscopic patterns that are similar across systems.