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 \textcolor{black}{graph} 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. The source code for reproducing the experimental results is available at https://github.com/daiquanyu/AdaGCN.
翻译:本文研究跨网络节点分类问题,以克服单一网络中标签数据不足的问题,目的是利用部分标签源网络中的标签信息,协助完全无标签或部分标签的目标网络中的节点分类。由于网络之间的域变换,单一网络学习的现有方法无法解决这一问题。一些多网络学习方法严重依赖跨网络连接的存在,因此无法适用于这一问题。为了解决这一问题,我们提议采用一个新型的\textcolor{black ⁇ g_graph}传输学习框架AdaGCN, 利用对称域适应和图解变技术。它由两个部分组成:半监督学习部分和对称域调整部分。前者的目的是学习类别中带有源和目标网络的标签信息的类别区分说明,而后者则有助于减少源和目标区域之间的分布差异,以促进知识转移。关于真实世界数据集的广泛经验评估表明,AdaGCN能够成功地传输源网络上低标签率的类信息,而源网络/方图变换的源/目标区域之间则有很大差异。