Title: Transferable Feature Learning on Graphs Across Visual Domains. Abstract: Unsupervised domain adaptation has attracted increasing attention in recent years, which adapts classifiers to an unlabeled target domain by exploiting a labeled source domain. To reduce discrepancy between source and target domains, adversarial learning methods are typically selected to seek domain-invariant representations by confusing the domain discriminator. However, classifiers may not be well adapted to such a domain-invariant representation space, as the sample-level and class-level data structures could be distorted during adversarial learning. In this paper, we propose a novel Transferable Feature Learning approach on Graphs (TFLG) for unsupervised adversarial domain adaptation, which jointly incorporates sample-level and class-level structure information across two domains. TFLG first constructs graphs for mini-batch samples, and identifies the class-wise correspondence across domains. A novel cross-domain graph convolutional operation is designed to jointly align the sample-level and class-level structures in two domains. Moreover, a memory bank is designed to further exploit the class-level information. Extensive experiments on benchmark datasets demonstrate the effectiveness of our approach, compared to the representative unsupervised domain adaptation methods.