Relational Active Learning for Joint Collective Classification Models.

In many network domains, labeled data may be costly to acquire—indicating a need for relational active learning methods. Recent work has demonstrated that relational model performance can be improved by taking network structure into account when choosing instances to label. However, in collective inference settings, both model estimation and prediction can be improved by acquiring a node's label—since relational models estimate a joint distribution over labels in the network and collective classification methods propagate information from labeled training data during prediction. This conflates improvement in learning with improvement in inference, since labeling nodes can reduce inference error without improving the overall quality of the learned model. Here, we use across-network classification to separate the effects on learning and prediction, and focus on reduction of learning error. When label propagation is used for learning, we find that labeling based on prediction certainty is more effective than labeling based on uncertainty. As such, we propose a novel active learning method that combines a network-based certainty metric with semi-supervised learning and relational resampling. We evaluate our approach on synthetic and real-world networks and show faster learning compared to several baselines, including the network based method of Bilgic et al. (2010).