Graph Transfer Learning via Adversarial Domain Adaptation with Graph Convolution

Quanyu Dai, Xiao-Ming Wu, Jiaren Xiao, Xiao Shen, Dan Wang

Abstract:

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 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.