Link-based Active Learning
Mustafa Bilgic, Lise Getoor · 2009
Supervised and semi-supervised data mining techniques require labeled data. However, labeling examples is costly for many real-world applications. To address this problem, active learning techniques have been developed to guide the labeling process in an effort to minimize the amount of labeled data without sacrificing 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. 1