Growing networks as models for information and social systems

Complex networks have been successfully used to represent a wide range of real systems. I will focus on growing information networks in particular where time plays a crucial role and which are relevant in many distinct fields such as bibliometrics (to model citations among scientific papers), e-commerce (to model users and their purchases in Amazon.com, for example), and information filtering in general (to model links among web sites, for example). I will discuss basic models of growing networks and their later improvements and generalizations. I will show that besides helping us to understand the evolution of real systems, these models allow us to understand the limitations of metrics and algorithms that we often use without knowing whether they are in fact appropriate for a particular system of our interest. For example, does it make sense to use Google's PageRank to rank scientific papers?