Network Transfer Learning via Adversarial Domain Adaptation with Graph Convolution

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. T o 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. It is an important building block of numerous real-world applications, such as product recommendation in e-commerce websites, advertisement distribution in social networks, and protein function identification for disease diagnosis. Many research efforts have been made to develop reliable and efficient methods for node classification in networked data. In the era of big data, massive amount of raw data in information networks is produced everyday . However, labeled data is significantly expensive and slow to acquire due to the high cost and long time of human annotations, making it difficult to train a well-generalized classifier [2]. Moreover, in some newly-formed networks such as a protein-protein interaction network constructed by some researchers, there may be no labels at all. Hence, it would be impossible to classify the nodes with only the information of this network. T o tackle these issues, a promising approach is to utilize class information from other similar or related networks to assist in classification, i.e., transfer learning on networked data [3], [4].