Node classification across networks via category-level domain adaptive network embedding

Boshen Shi, Yongqing Wang, Jiangli Shao, Huawei Shen, Yangyang Li, Xueqi Cheng

Abstract: To improve the performance of classifying nodes on unlabeled or scarcely-labeled networks, the task of node classification across networks is proposed for transferring knowledge from similar networks with rich labels. As data distribution shift exists across networks, domain adaptive network embedding is proposed to overcome such challenge by learning network-invariant and discriminative node embeddings, in which domain adaptation technique is applied to network embedding for reducing domain discrepancy. However, existing works merely discuss category-level domain discrepancy which is crucial to better adaptation and classification. In this paper, we propose category-level domain adaptive network embedding. The key idea is minimizing intra-class domain discrepancy and maximizing inter-class domain discrepancy between source and target networks simultaneously. To further enhance classification performance on target network, we reduce embedding variation inside each class and enlarge it between different classes. Graph attention network is adopted for learning network embeddings. In addition, a novel pseudo-labeling strategy for target network is developed to better compute category-level information. Theoretical analysis guarantees the effectiveness of our model. Furthermore, extensive experiments on real-world datasets show that our model achieves the state-of-art performance, in particular, outperforming existing domain adaptive network embedding models by up to 32%.