Using social network theory for modeling human mobility

Human mobility modeling plays an essential role in accurately understanding the performance of data forwarding protocols in mobile networks and has been attracting increasing research interest in recent years. People's movement behaviors are strongly affected by their social interactions with each other, which, however, are not sufficiently considered in most existing mobility models. Recent studies in social network theory have provided many theoretical and experimental results, which are useful and powerful for modeling the realistic social dimension of human mobility. In this article we present a novel human mobility model based on heterogeneous centrality and overlapping community structure in social networks. Instead of extracting communities from artificially generated social graphs, our model manages to construct the k-clique overlapping community structure which satisfies the common statistical features observed from distinct real social networks. This approach achieves a good trade-off between complexity and reality. The simulation results of our model exhibit characteristics observed from real human motion traces, especially heterogeneous human mobility popularity, which has significant impact on data forwarding schemes but has never been considered by existing mobility models.