This article presents a new simulation method to make global network inference from sampled data. The proposed method takes sampled ego network data and uses exponential random graph models (ERGM) to reconstruct the features of the true, unknown network. After describing the method, the author presents two validity checks of the approach: the first uses the 20 largest Add Health networks while the second uses the Sociology Coauthorship network in the 1990s. For each test, I take random ego network samples from the known networks and use my method to make global network inference. The method successfully reproduces the properties of the networks, such as distance and main component size. The results also suggest that simpler, baseline models provide considerably worse estimates for most network properties. The paper concludes with a discussion of the bounds/limitations of ego network sampling as well as possible extensions to the proposed approach.
ASJC Scopus subject areas
- Sociology and Political Science