This paper studies the problem of cross-network node classification to overcome the insufficiency of labeled data in a single network. It aims to leverage the label information in a partially labeled source network to assist node classification in a completely unlabeled or partially labeled target network. Existing methods for single network learning cannot solve this problem due to the domain shift across networks. Some multi-network learning methods heavily rely on the existence of cross-network connections, thus are inapplicable for this problem. To tackle this problem, we propose a novel network transfer learning framework AdaGCN by leveraging the techniques of adversarial domain adaptation and graph convolution. It consists of two components: a semi-supervised learning component and an adversarial domain adaptation component. The former aims to learn class discriminative node representations with given label information of the source and target networks, while the latter contributes to mitigating the distribution divergence between the source and target domains to facilitate knowledge transfer. Extensive empirical evaluations on real-world datasets show that AdaGCN can successfully transfer class information with a low label rate on the source network and a substantial divergence between the source and target domains. Codes will be released upon acceptance.

本文研究跨网络节点分类问题，提出一种基于对抗领域自适应和图卷积的图传输学习框架 AdaGCN 以在标签数据不足的情况下，从部分标记的源网络中利用标记信息辅助完全未标记或只有部分标记的目标网络节点分类，并在真实数据集上进行了大量实验评估，并验证了 AdaGCN 在标签率低和源目标领域差异显著的情况下都能成功传输类信息。

通过图卷积的对抗领域自适应实现图传输学习