Most inference for models for social networks assumes that the presence or absence of all links in the network are completely observed, that the information is completely reliable and there are no measurement (e.g. recording) errors. This is clearly not true in practice, as the majority of network data is collected though sample surveys. In addition even if a census of a population is attempted, individuals and links between individuals are missed (i.e., do not appear in the recorded data).
In this talk we develop the conceptual and computational theory for inference based on sampled network information. We first review forms of network sampling designs used in practice and consider the various forms of out-of-design missing data. We consider inference from the likelihood framework, and develop a typology of network data that reflects their treatment within this frame. We then develop inference for social network models based on information from adaptive network mechanisms.
The ideas are motivated and illustrated by a network survey in Colorado Springs designed to understand the social determinants of HIV spread.
