| Abstract | Supervised and semi-supervised data mining techniques require labeled data.However, labeling examples is costly for many real-world applications. To ad-
dress this problem, active learning techniques have been developed to guide the
labeling process in an effort to minimize the amount of labeled data without sac-
rificing much from the quality of the learned models. Yet, most of the active
learning methods to date have remained relatively agnostic to the rich structure
offered by network data, often ignoring the relationships between the nodes of a
network. On the other hand, the relational learning community has shown that the
relationships can be very informative for various prediction tasks. In this paper,
we propose different ways of adapting existing active learning work to network
data while utilizing links to select better examples to label.
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