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Activity Number:
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496
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Type:
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Invited
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Date/Time:
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Thursday, August 7, 2008 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Health Policy Statistics
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| Abstract - #300097 |
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Title:
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Spatial Process Model for Social Network Analysis
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Author(s):
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Crystal Linkletter*+
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Companies:
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Brown University
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Address:
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Box G-S121-7, Providence, RI, 02912,
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Keywords:
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social networks ; Bayesian inference ; spatial process
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Abstract:
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There has been a recent increase in the use of network models for representing interactions and structure in many complex systems. Here we propose using a latent spatial process model for the statistical analysis of networks, emphasizing applications to social networks. This approach smoothes the relationship between connections and covariates in a sample network with relatively few parameters, allowing probabilities of connection for a population to be inferred. One important application of such a predictive network model is to improve understanding of how an infectious disease might spread in a population, since local network topology can have a significant impact on contact-based processes.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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