Modeling Networks When Data Is Missing or Sampled
Presented by Mark S. Handcock - University of California, Los Angeles, Department of Statistics
Date: Thursday, October 11th, 2012
Time: 10:30 AM – 12:00 PM Pacific / 1:30 PM – 3:00 PM Eastern
Host Location: Santa Monica, Forum 1226
Other Locations: Pittsburgh, room 6202 & Washington, DC, room 7128
Network models are widely used to represent relational information among interacting units and the structural implications of these relations. Recently, social network studies have focused a great deal of attention on random graph models of networks whose nodes represent individual social actors and whose edges represent a specified relationship between the actors. Most inference for social network models assumes that the presence or absence of all possible links is observed, that the information is completely reliable, and that there are no measurement (e.g. recording) errors. This is clearly not true in practice, as much 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 partially observed network information. We first review forms of network sampling designs used in practice. 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 designs. We motivate and illustrate these ideas by analyzing the effect of link-tracing sampling designs on a collaboration network, and of missing data in a friendship network among adolescents. This is joint work with Krista J. Gile, University of Massachusetts, Amherst.
About the Presenter
Dr. Mark S. Handcock is Professor of Statistics in the Department of Statistics at the University of California - Los Angeles. His research involves methodological development, and is based largely on motivation from questions in the social and epidemiological sciences. He has published extensively on survey sampling, network inference, and network sampling methods. He moved to UCLA from the University of Washington in 2010. For details see his web page http://www.stat.ucla.edu/~handcock.
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