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2011 Sessions
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2011 Sessions
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2011 Sessions
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2011 Sessions
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2011 Sessions
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2011 Sessions
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2011 Sessions
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2011 Sessions
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2011 Sessions
Module
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| Instructor: Jeff Smith |
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Location: BE-252 |
This is a 1-day workshop providing a tutorial on exponential random graph models for social networks, emphasizing a hands-on approach to fitting these models to data. Exponential random graph (ERG) models provide a flexible framework for representing the complex dependencies within relational data structures. Examples include degree distributions and stars, node attribute-based mixing patterns, triadic patterns that lead to clustering, and other systematic network configurations. These are not descriptive analytical tools, but instead generative models. The parameters can be interpreted in terms of the propensities for actors to form specific configurations of ties.
The hands-on portion will utilize ERGM, a module in StatNet, a statistical package for the visualization, analysis and simulation of social network data written for the R statistical computing environment. The statnet package has a flexible user interface, can handle relatively large networks, and supports directed, undirected and bipartite network analysis. The package provides tools for both model estimation and model-based network simulation, with visualization, tools for inference and validation, and goodness of fit diagnostics. The package runs on any computing platform that supports R (Windows, Unix/Linux, Mac), it is freely available through the Comprehensive R Archive Network (CRAN), and it has a seamless interface to SNA (an R package for traditional network analysis written by Carter Butts).
The google group has a collection of datasets and R programs in a zip file. Participants should download the zip file and extract the files into one folder.
Robins, G., P. Pattison, Y. Kalish, and D. Lusher (2007). On exponential random graph models for cross-sectional analysis of complete networks: An introduction to exponential random graph (p*) models for social networks. Social Networks, 29(2): 173-191 [pdf]
For more information about the content of this course, contact Jeff Smith
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