  Predicting pairs of anchor users plays an important role in the cross-network
analysis. Due to the expensive costs of labeling anchor users for training
prediction models, we consider in this paper the problem of minimizing the
number of user pairs across multiple networks for labeling as to improve the
accuracy of the prediction. To this end, we present a deep active learning
model for anchor user prediction (DALAUP for short). However, active learning
for anchor user sampling meets the challenges of non-i.i.d. user pair data
caused by network structures and the correlation among anchor or non-anchor
user pairs. To solve the challenges, DALAUP uses a couple of neural networks
with shared-parameter to obtain the vector representations of user pairs, and
ensembles three query strategies to select the most informative user pairs for
labeling and model training. Experiments on real-world social network data
demonstrate that DALAUP outperforms the state-of-the-art approaches.
