# Domain-adversarial Network Alignment

## TL;DR

This paper introduces DANA, a deep adversarial network architecture that learns domain-invariant representations for network alignment, improving accuracy by reducing domain bias.

## Contribution

The paper presents a unified deep adversarial framework using graph convolutional networks for network alignment that effectively minimizes domain bias and includes several model variants.

## Key findings

- Achieves state-of-the-art results on real-world datasets.
- Effectively reduces domain representation bias.
- Demonstrates versatility with multiple model variants.

## Abstract

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.