# DANE: Domain Adaptive Network Embedding

## TL;DR

DANE introduces a domain adaptive network embedding method that uses graph convolutional networks and adversarial learning to produce transferable node representations across multiple networks, enabling effective cross-network tasks.

## Contribution

It proposes a novel framework combining graph convolutional networks and adversarial regularization for domain-adaptive network embedding, with theoretical guarantees.

## Key findings

- Outperforms state-of-the-art in cross-network tasks
- Effective transferability of embeddings across different networks
- Theoretical guarantees for embedding transferability

## Abstract

Recent works reveal that network embedding techniques enable many machine learning models to handle diverse downstream tasks on graph structured data. However, as previous methods usually focus on learning embeddings for a single network, they can not learn representations transferable on multiple networks. Hence, it is important to design a network embedding algorithm that supports downstream model transferring on different networks, known as domain adaptation. In this paper, we propose a novel Domain Adaptive Network Embedding framework, which applies graph convolutional network to learn transferable embeddings. In DANE, nodes from multiple networks are encoded to vectors via a shared set of learnable parameters so that the vectors share an aligned embedding space. The distribution of embeddings on different networks are further aligned by adversarial learning regularization. In addition, DANE's advantage in learning transferable network embedding can be guaranteed theoretically. Extensive experiments reflect that the proposed framework outperforms other state-of-the-art network embedding baselines in cross-network domain adaptation tasks.