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Activity Number:
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618
- Modeling Extremes in Weather, Networks, and Finance
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 2, 2018 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Risk Analysis
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Abstract #328805
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Presentation
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Title:
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Fitting the Linear Preferential Attachment Model
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Author(s):
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Sidney I. Resnick* and Phyllis Wan and Richard A. Davis and Tiandong Wang
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Companies:
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Cornell University and Columbia University and Columbia University and School of Operations Research and Information Engineering
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Keywords:
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calibrate;
preferential attachment;
power laws;
multivariate heavy tail
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Abstract:
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Preferential attachment is a mechanism for modeling power-law behavior of the degree distributions in directed social networks. We consider methods for fitting a 5-parameter linear preferential model to network data under two data scenarios. In the case where full history of the network formation is given, we give the maximum likelihood estimator of the parameters and show that they are strongly consistent and asymptotically normal. In the case where only a {single-time} snapshot of the network is available, we propose an estimation method which combines method of moments with an approximation to the likelihood. The resulting estimator is also strongly consistent and performs well compared to the MLE estimator. We illustrate both estimation procedures using simulated data, and explore the usage of this model in a real data example. (Joint with P. Wan, R. Davis, T. Wang)
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Authors who are presenting talks have a * after their name.
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