Network sampling poses a radical idea: that it is possible to measure global network structure without the full population coverage assumed in most network studies. Network sampling is only useful, however, if a researcher can produce accurate global network estimates. This article explores the practicality of making network inference, focusing on the approach introduced in Smith (2012). The method uses sampled ego network data and simulation techniques to make inference about the global features of the true, unknown network. The validity check here includes more difficult scenarios than previous tests, including those that go beyond the initial scope conditions of the method. I examine networks with a skewed degree distribution and surveys that limit the number of social ties a respondent can list. For each network/survey combination, I take a random ego network sample, run the simulation method, and compare the results to the true values (using measures of connectivity and cohesion). I also test the method on local measures of network structure. The results, on the whole, are encouraging. The method produces good estimates even in cases where the degree distribution is skewed and the survey is strongly restricted. I also find that is it better to not truncate the survey if possible. If the survey must be restricted, the researcher would do well to infer the missing data, rather than use the raw data naively.
- exponential random graph model (ERGM)
- social networks
ASJC Scopus subject areas
- Algebra and Number Theory
- Social Sciences (miscellaneous)
- Sociology and Political Science