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The field of complex network exploded since the 1990s, the number of publications in a variety of different areas has grown exponentially and practically, and every discipline started to recognize the presence of these mathematical structures in its area of research. Actually almost any system from the nowadays traditional example of the Internet to complex patterns of metabolic reactions can be analyzed through the graph theory. In its simplest and non rigorous definition a graph is a mathematical object consisting of a set of elements (vertices) and a series of links between these vertices (edges). This is of course a very general description, and as any mathematical abstraction, the idea is to discard many of the particular properties of the phenomenon studied. Nevertheless, this modeling is remarkably accurate for a variety of situations. Vertices can be persons related by friendship or acquaintances relations. Vertices can be proteins connected with one another if they interact in the cell. Networks have always existed in Nature of course, but it is fair to say that given the present technological explosion, they became more and more important. Starting from the Internet the web of connections between computers we started to link and share our documents through web applications and we start to get connected with a number of persons larger than usual. It is this revolution in our daily habit that made natural thinking of networks in science and research. Once this has been realized it became natural to see the cell as a network of molecular events from chemical reactions to gene expressions. The point is to establish if this new perspective can help researchers in finding new results and by understanding the development of these phenomena and possibly control their evolution. We believe that this is the case and in the following we shall provide the evidence of that. Together with applications there are of course true scientific questions attached to network theory. Consider the various ways in which the edges are distributed among the vertices: even by keeping the number of edges and vertices constant we have many different patterns possible. Interestingly some features used to describe these shapes are not related to the particular example considered, but instead they are universal. That is to say they can be found in almost any network around.

 

In this book, we introduce the subject of complex networks and we present the structure of the associated topics that range from social science to biology and finance. We start by considering the mathematical foundations of networks and we then move to an overview of the various applications
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