Similarity analysis and modeling in mobile societies
Gautam Malviya Thakur, Ahmed Helmy, Wei-jen Hsu · 2010
A new generation of "behavior-aware" delay tolerant networks is emerging in what may define future mobile social networks. With the introduction of novel behavior-aware protocols, services and architectures, there is a pressing need to understand and realistically model mobile users behavioral characteristics, their similarity and clustering. Such models are essential for the analysis, performance evaluation, and simulation of future DTNs. This paper addresses issues related to mobile user similarity, its definition, analysis and modeling. To define similarity, we adopt a behavioral-profile based on users location preferences using their on-line association matrix and its SVD, then calculate the behavioral distance to capture user similarity. This measures the difference of the major spatio-temporal behavioral trends and can be used to cluster users into similarity groups or communities. We then analyze and contrast similarity distributions of mobile user populations in two settings: (i) based on real measurements from four major campuses with over ten thousand users for a month, and (ii) based on existing mobility models, including random direction and time-varying community models.