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.

本文提出了一种全新的领域自适应网络嵌入框架(Domain Adaptive Network Embedding framework)，利用图卷积网络来学习可转移的嵌入。在该框架中，多网络的节点通过一组共同的可学习参数编码为向量，使向量共享一个对齐嵌入空间。并通过对抗学习正则化来进一步对齐不同网络上的嵌入分布。此外，DANE在学习可转移网络嵌入方面的优势可以在理论上得到保证。广泛的实验表明，所提出的框架在跨网络领域自适应任务中优于其他最先进的网络嵌入基线。

DANE: 域自适应网络嵌入