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Complex networks describe a wide range of systems in fields as disparate as sociology, biol-ogy and technology. Examples include networks of acquaintances or collaborations between individuals, neural networks in the brain, or computers in telecommunication networks. The past ten years have witnessed an increasingly large emphasis in the physics com-munity on the study of complex networks. This interest has certainly been triggered by the optimized rating of computing facilities, and by the grown availability of data on large real world networks (such as the Internet and the World Wide Web, electric power grids, phone call networks, the actors ’ collaboration network in movie databases, scientific coauthorship and citation networks from the Science Citation Index, but also systems of interest in biol-ogy and medicine, as cortical networks or metabolic and protein networks). The massive and comparative analysis of networks from different fields has produced a dramatic advance in the understanding of complex systems. The first issue handled is on the structure of complex networks. In particular, we have learned that, despite the inherent differences, most of the real world networks are charac-terized by similar topological properties, as for instance, relatively small characteristic pat