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Abstract Details
Activity Number:
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180
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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IMS
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Abstract - #306518 |
Title:
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Asymptotic Inference for Preferential Attachment Models
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Author(s):
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Daniel Saxton*+ and Anand N. Vidyashankar and Huzefa Rangwala
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Companies:
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and George Mason University and George Mason University
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Address:
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5415 Helm Court, Fairfax, VA, 22032, United States
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Keywords:
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prefential attachment ;
branching processes ;
scaling exponent ;
power law ;
Twitter data
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Abstract:
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Preferential attachment models and their variants are being increasingly used to model evolving networks in various scientific fields such as social networks, neuroscience, metabolic systems, finance, and economics. These models possess several interesting properties including the so-called power law for the degree distribution. However, inference concerning various aspects of such evolving networks have not been well investigated.
In this talk we will present statistical methods to describe various features of the network topology when modeled using preferential attachment models. Specifically, we will describe methods to estimate (i) the scaling exponent of the degree distribution, (ii) clustering coefficient, and (iii) the diameter of the network. We will describe their large sample properties and study their behavior in small networks using simulations. Finally, we apply our methods to a recent Tweets2011 data obtained from NIST.
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