The main goal of our research was to characterize the individuals connected in a social network by analyzing the local structure of the network. For that, we proposed a method that describes the way a node (corresponding to an individual) is embedded in the network. Our method is related to the analysis of egocentred networks in sociology and to the local approach in the study of complex networks. It can be applied to small networks, to fractions of networks and also to large networks, due to its small complexity. We applied the proposed method to two large social networks, one modeling online activity on MySpace, the other one modeling mobile phone communications. In the first case we were interested in analyzing the online popularity of artists on MySpace. In the second case, we proposed and used a method for clustering nodes that are connected in a similar way to the network. We found that the distribution of mobile phone users into clusters was correlated to other characteristics of the individuals (i.e. communication intensity and age). Although in this thesis we applied the two methods only to social networks, they can be applied in the same way to any other graph, no matter its origin.
