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Adjusting for Network Size and Composition Effects in Exponential-Family Random Graph Models.
Pavel N Krivitsky1, Mark S Handcock, Martina Morris
1Department of Statistics and iLab at H. John Heinz III College, Carnegie Mellon University, Pittsburgh, USA.
Statistical Methodology
|June 22, 2011
Summary
Exponential-family random graph models (ERGMs) can now better model social networks. A new method preserves mean degree, improving analysis of egocentrically sampled data like the NHSLS.
Area of Science:
- Social Network Analysis
- Statistical Modeling
- Computational Social Science
Background:
- Exponential-family random graph models (ERGMs) are used for simulating social networks.
- Standard ERGMs preserve network density, which is often unsuitable for real-world social structures.
- Existing models struggle with increasing network size and changing composition.
Purpose of the Study:
- To propose a modification to ERGMs that preserves mean degree instead of density.
- To enable ERGMs for analyzing egocentrically sampled network data.
- To improve the applicability of ERGMs to dynamic social networks.
Main Methods:
- Introduced a modification to ERGMs using an offset term.
- Demonstrated the model's ability to preserve mean degree asymptotically.
- Applied the modified ERGMs to egocentrically sampled data.
Main Results:
- The modified ERGMs successfully preserve mean degree as network size increases.
- The approach accommodates changes in network composition.
- The method is validated using data from the National Health and Social Life Survey (NHSLS).
Conclusions:
- The proposed ERGM modification offers a more appropriate way to model social networks.
- This advancement enhances the analysis of egocentrically sampled network data.
- The findings contribute to more robust social network modeling techniques.
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