Collection and analysis of multi-dimensional network data for opportunistic networking research

Opportunistic networks use human mobility and consequent wireless contacts between mobile devices to disseminate data in a peer-to-peer manner. Designing appropriate algorithms and protocols for such networks is challenging as it requires understanding patterns of (1) mobility (who meets whom), (2) social relations (who knows whom) and (3), communication (who communicates with whom). To date, apart from few small test setups, there are no operational opportunistic networks where measurements could reveal the complex correlation of these features of human relationships. Hence, opportunistic networking research is largely based on insights from measurements of either contacts, social networks, or communication, but not all three combined. In this paper we analyze two datasets comprising social, mobility and communication ties. The first dataset we have collected with Stumbl, a Facebook application that lets participating users report their daily face-to-face meetings with other Facebook friends. It also logs user interactions on Facebook (e.g. comments, wall posts, likes). For the second dataset, we use data from two online social networks (Twitter and Gowalla) on the same set of nodes to infer social, communication and mobility ties. We look at the interplay of the different dimensions of relationships on a pairwise level and analyze how the network structures compare to each other.