Abstract
One way to describe complex interactions between different elements in a system is through complex network analysis. This approach has been applied to many real networks, such as WWW, protein-protein interaction, metabolic and social networks. In addition, it has been established that complex networks have certain common characteristics with non-linear time series such as fractality and self similarity, i.e. invariance under change of scale. In this work, we have developed a general framework to transform complex networks into non-linear time series (and vice-versa). This has allowed us to explore new research avenues that this dual vision of complex phenomena may provide. As examples we have chosen standard case studies from non-linear dynamical systems, which we have complemented with a more applied case from metabolic networks. Preliminary results are the development of a new type of complex network: chaotic networks and the analysis of the statistical properties of time series generated by several network structures. This work is a preliminary step to focus the research and to assess the most important avenues to explore more in depth.