Network analysis and modeling address a wide spectrum of techniques for studying domains consisting of individuals that are linked together into complex networks. Networks refer to artificial and natural systems like communication networks, social networks and biological networks. They constitute a very active area of research in a variety of scientific disciplines, including Physics, Biology, Artificial Intelligence and Mathematics. Both graph theory and techniques recently developed for the analysis of networks provide a substantial background for studying complex network structures and dynamics in artificial and biological systems. They allow us to answer questions in common to these networks like aspects of adaptability, error and attack tolerance, complexity, community structures, and propagation patterns. One of the key features of natural networks is their ability to adapt to changing environments, maintaining an appropriate pattern of behaviour. Examples of such adaptive capacity span the whole range of natural networks, from gene-protein interaction networks within individual cells, through physiological systems, to ecosystems. The aim of this symposium was to provide a forum to bring together researchers in biology, computer science and related disciplines in order to discover related mechanisms in natural and artificial networks and to initiate, combine and promote research in both fields.