Abstract

Abstract—The availability of massive network and mobility data from diverse domains has fostered the analysis of human be-haviors and interactions. This data availability leads to challenges in the knowledge discovery community. Several different analyses have been performed on the traces of human trajectories, such as understanding the real borders of human mobility or mining social interactions derived from mobility and viceversa. However, the data quality of the digital traces of human mobility has a dramatic impact over the knowledge that it is possible to mine, and this issue has not been thoroughly tackled so far in literature. In this paper, we mine and analyze with complex network techniques a large dataset of human trajectories, a GPS dataset from more than 150k vehicles in Italy. We build a multiresolution spatial grid and we map the trajectories to several complex networks, by connecting the different areas of our region of interest. We also analyze different temporal slices of the network, obtaining a dynamic perspective over its evolution. We analyze the structural properties of the temporal and geographical slices and their human mobility predictive power. The result is a significant advancement in our understanding of the data transformation process that is needed to connect mobility with social network analysis and mining. I.