During the last decade, the study of large scale networks has attracted a large amount of attention and works from several domains: sociology, biology, computer science, epidemiology. Consequently, complex networks have become a new area of research. This emerging domain has proposed a large set of tools that can be used on any complex network in order to get a deep insight on its properties and to compare it to other networks. Such fundamental properties are used as characterization parameters in the study of various problems such as virus spreading in the epidemiology context, or information/innovation diffusion for instance. However, a fundamental property of complex networks has been, until recently, less studied: the evolution in time, i.e., their dynamical aspect. Indeed most complex networks change, new nodes and edges appear while some other disappear, and in all the scientific domains cited above, the dynamic is an intrinsic property: people make new acquaintances, change their relations, new machines are added on the Internet, communication links fail, etc. Therefore, it appears crucial to better understand the intrinsic characteristics of such dynamic complex networks, first to get knowledge but also to be able to simulate them.