  Research in statistical relational learning has produced a number of methods
for learning relational models from large-scale network data. While these
methods have been successfully applied in various domains, they have been
developed under the unrealistic assumption of full data access. In practice,
however, the data are often collected by crawling the network, due to
proprietary access, limited resources, and privacy concerns. Recently, we
showed that the parameter estimates for relational Bayes classifiers computed
from network samples collected by existing network crawlers can be quite
inaccurate, and developed a crawl-aware estimation method for such models
(Yang, Ribeiro, and Neville, 2017). In this work, we extend the methodology to
learning relational logistic regression models via stochastic gradient descent
from partial network crawls, and show that the proposed method yields accurate
parameter estimates and confidence intervals.
