  Network alignment is a critical task to a wide variety of fields. Many
existing works leverage on representation learning to accomplish this task
without eliminating domain representation bias induced by domain-dependent
features, which yield inferior alignment performance. This paper proposes a
unified deep architecture (DANA) to obtain a domain-invariant representation
for network alignment via an adversarial domain classifier. Specifically, we
employ the graph convolutional networks to perform network embedding under the
domain adversarial principle, given a small set of observed anchors. Then, the
semi-supervised learning framework is optimized by maximizing a posterior
probability distribution of observed anchors and the loss of a domain
classifier simultaneously. We also develop a few variants of our model, such
as, direction-aware network alignment, weight-sharing for directed networks and
simplification of parameter space. Experiments on three real-world social
network datasets demonstrate that our proposed approaches achieve
state-of-the-art alignment results.
