Similarity Analysis and Modeling in Mobile Societies: The Missing Link - Computer Science > Networking and Internet ArchitectureReportar como inadecuado

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Abstract: A new generation of -behavior-aware- delay tolerant networks is emerging inwhat may define future mobile social networks. With the introduction of novelbehavior-aware protocols, services and architectures, there is a pressing needto 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 addressesissues related to mobile user similarity, its definition, analysis andmodeling. To define similarity, we adopt a behavioral-profile based on userslocation preferences using their on-line association matrix and its SVD, thencalculate the behavioral distance to capture user similarity. This measures thedifference of the major spatio-temporal behavioral trends and can be used tocluster users into similarity groups or communities. We then analyze andcontrast similarity distributions of mobile user populations in two settings:i based on real measurements from four major campuses with over ten thousandusers for a month, and ii based on existing mobility models, including randomdirection and time-varying community models. Our results show a rich set ofsimilar communities in real mobile societies with distinct behavioral clustersof users. This is true for all the traces studied, with the trend beingconsistent over time. Surprisingly, however, we find that the existing mobilitymodels do not explicitly capture similarity and result in homogeneous usersthat are all similar to each other. Thus the richness and diversity of userbehavioral patterns is not captured to any degree in the existing models. Thesefindings strongly suggest that similarity should be explicitly captured infuture mobility models, which motivates the need to re-visit mobility modelingin the future.

Autor: Gautam Thakur, Ahmed Helmy, Wei-Jen Hsu



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