Abstract
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-theart approaches