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Abstract Details
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Activity Number:
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170
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Type:
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Contributed
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Date/Time:
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Monday, August 1, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #303035 |
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Title:
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Retrospective Sampling in Social Networks
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Author(s):
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Sudeshna Paul*+ and A. James O'Malley
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Companies:
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Harvard Medical School and Harvard Medical School
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Address:
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Department of Health Care Policy, Boston, MA, 02115-5899,
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Keywords:
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social networks ;
dyadic independence ;
tie-formation ;
tie-dissolution ;
sparse ;
retrospective sampling
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Abstract:
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Statistical modeling and analysis of dynamic networks has been a challenging area due to the complex structure and lack of manageable data. Recently, we proposed a log-linear model based on contingency tables to model transition probabilities of the status of the relationship between pairs of individuals (dyads). Problems arise when applying this model to large networks with sparse connections. Frequently, the cell representing null (no relationship) to null transitions dominates the contingency table and in extreme cases may lead to nearly inestimable models. To partially overcome this difficulty, we propose to fit the model on a subset of dyads in the complete network that includes all ``non-null' dyads across time but only a random sample of ``always-null' dyads. Methods are illustrated using simulated and real data.
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