  Abundant real-world data can be naturally represented by large-scale
networks, which demands efficient and effective learning algorithms. At the
same time, labels may only be available for some networks, which demands these
algorithms to be able to adapt to unlabeled networks. Domain-adaptive hash
learning has enjoyed considerable success in the computer vision community in
many practical tasks due to its lower cost in both retrieval time and storage
footprint. However, it has not been applied to multiple-domain networks. In
this work, we bridge this gap by developing an unsupervised domain-adaptive
hash learning method for networks, dubbed UDAH. Specifically, we develop four
{task-specific yet correlated} components: (1) network structure preservation
via a hard groupwise contrastive loss, (2) relaxation-free supervised hashing,
(3) cross-domain intersected discriminators, and (4) semantic center alignment.
We conduct a wide range of experiments to evaluate the effectiveness and
efficiency of our method on a range of tasks including link prediction, node
classification, and neighbor recommendation. Our evaluation results demonstrate
that our model achieves better performance than the state-of-the-art
conventional discrete embedding methods over all the tasks.
