The role of assortativity in real-world and on-line social networks has been largely investigated in the literature, in which several forms of assortativity have been analyzed. However, all these forms are not able to capture some pieces of knowledge that are strategic when moving from a single-social-network to a multiple-social-network perspective. The relevance of such a point of view is strongly increasing, due to the interaction among users, applications, information flows of different social networks. This interaction is the key feature of a new emerging paradigm called social internetworking scenario. Here, all the knowledge concerning information crossing different social networks assumes high importance. This stimulates the study of assortativity under the social internetworking scenario perspective. In this paper, we propose a new notion of assortativity that captures some important aspects concerning the above issue. Furthermore, we give an effective methodology for its computation. A deep experimental analysis has been performed aimed to measure the new form of assortativity for Facebook w.r.t. other seven (top-level) on-line social networks.