Graph Transfer Learning via Adversarial Domain Adaptation with Graph Convolution

Understand

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.