TY - GEN
T1 - Mobile phone graph evolution
T2 - 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
AU - Liu, Siyuan
AU - Li, Lei
AU - Faloutsos, Christos
AU - Ni, Lionel M.
PY - 2011
Y1 - 2011
N2 - What are the features of mobile phone graph along the time? How to model these features? What are the interpretation for the evolutional graph generation process? To answer the above challenging problems, we analyze a massive who-call-whom networks as long as a year, gathered from records of two large mobile phone communication networks both with 2 million users and 2 billion of calls.We examine the calling behavior distribution at multiple time scales (e.g., day, week, month and quarter), and find that the distribution is not only skewed with a heavy tail, but also changing at different time scales. How to model the changing behavior, and whether there exists a distribution fitting the multi-scale data well? In this paper, first, we define a d-stable distribution and a Multi-scale Distribution Fitting (MsDF) problem. Second, to analyze our observed distributions at different time scales, we propose a framework, ScalePower, which not only fits the multi-scale data distribution very well, but also works as a convolutional distribution mixture to explain the generation mechanism of the multi-scale distribution changing behavior. Third, ScalePower can conduct a fitting approximation from a small time scale data to a large time scale. Furthermore, we illustrate the interesting and appealing findings from our ScalePower model and large scale real life data sets.
AB - What are the features of mobile phone graph along the time? How to model these features? What are the interpretation for the evolutional graph generation process? To answer the above challenging problems, we analyze a massive who-call-whom networks as long as a year, gathered from records of two large mobile phone communication networks both with 2 million users and 2 billion of calls.We examine the calling behavior distribution at multiple time scales (e.g., day, week, month and quarter), and find that the distribution is not only skewed with a heavy tail, but also changing at different time scales. How to model the changing behavior, and whether there exists a distribution fitting the multi-scale data well? In this paper, first, we define a d-stable distribution and a Multi-scale Distribution Fitting (MsDF) problem. Second, to analyze our observed distributions at different time scales, we propose a framework, ScalePower, which not only fits the multi-scale data distribution very well, but also works as a convolutional distribution mixture to explain the generation mechanism of the multi-scale distribution changing behavior. Third, ScalePower can conduct a fitting approximation from a small time scale data to a large time scale. Furthermore, we illustrate the interesting and appealing findings from our ScalePower model and large scale real life data sets.
UR - http://www.scopus.com/inward/record.url?scp=84863125237&partnerID=8YFLogxK
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U2 - 10.1109/ICDMW.2011.123
DO - 10.1109/ICDMW.2011.123
M3 - Conference contribution
AN - SCOPUS:84863125237
SN - 9780769544090
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 323
EP - 330
BT - Proceedings - 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
Y2 - 11 December 2011 through 11 December 2011
ER -