A complex network analysis of human mobility

Opportunistic networks use human mobility and consequent wireless contacts between mobile devices, to disseminate data in a peer-to-peer manner. To grasp the potential and limitations of such networks, as well as to design appropriate algorithms and protocols, it is key to understand the statistics of contacts. To date, contact analysis has mainly focused on statistics such as inter-contact and contact distributions. While these pair-wise properties are important, we argue that structural properties of contacts need more thorough analysis. For example, communities of tightly connected nodes, have a great impact on the performance of opportunistic networks and the design of algorithms and protocols. In this paper, we propose a methodology to represent a mobility scenario (i.e., measured contacts) as a weighted contact graph, where tie strength represents how long and often a pair of nodes is in contact. This allows us to analyze the structure of a scenario using tools from complex network analysis and graph theory (e.g., community detection, connectivity metrics). We consider four mobility scenarios of different origins and sizes. Across all scenarios, we find that mobility shows typical small world characteristics (short path lengths, and high clustering coefficient). Using state-of-the-art community detection, we also find that mobility is strongly modular. However, communities are not homogenous entities. Instead, the distribution of weights and degrees within a community is similar to the global distribution of weights, implying a rather intricate intra-community structure. To the best of our knowledge, this is the most comprehensive study of structural characteristics of wireless contacts, in terms of the number of nodes in our datasets, and the variety of metrics we consider. Finally, we discuss the primary importance of our findings for mobility modeling and especially for the design of opportunistic network solutions.