NDLI: Similarity analysis and modeling in mobile societies: the missing link

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Similarity analysis and modeling in mobile societies: the missing link

Content Provider	ACM Digital Library
Author	Thakur, Gautam S. Helmy, Ahmed Hsu, Wei-Jen
Abstract	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. Our results show a rich set of similar communities in real mobile societies with distinct behavioral clusters of users. This is true for all the traces studied, with the trend being consistent over time. Surprisingly, however, we find that the existing mobility models do not explicitly capture similarity and result in homogeneous users that are all similar to each other. Thus the richness and diversity of user behavioral patterns is not captured to any degree in the existing models. These findings strongly suggest that similarity should be explicitly captured in future mobility models, which motivates the need to re-visit mobility modeling to incorporate accurate behavioral models in the future.
Starting Page	13
Ending Page	20
Page Count	8
File Format	PDF
ISBN	9781450301398
DOI	10.1145/1859934.1859938
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-09-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Trace analysis Behavior-aware dtns Similarity Clustering Mobility modeling
Content Type	Text
Resource Type	Article

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