Adaptive Attribute Completion with Representation Space for Incomplete Graph Domain Adaptation

Graph Domain Adaptation (GDA) has emerged as an effective technique for transferring knowledge from a label-rich source graph to an unlabeled target graph by mitigating cross-graph discrepancies. However, in real-world scenarios, the attributes of partial nodes may be unobserved (i.e., missing) due to factors such as privacy protection. This inevitably exacerbates the distribution discrepancy of nodes between graph domains, making it more challenging to transfer effective knowledge to the target graph. To tackle this issue, we propose an adaptive attribute completion approach with representation space for incomplete graph domain adaptation, namely AC-GDA. It mutually assists the attribute completion and dynamically optimizes cross-graph trustworthy embeddings in an iterative manner, promoting positive knowledge migration while completing attributes. Specifically, we first propose a joint completion mechanism guided by topology similarity, which leverages cross-graph trustworthy attribute embeddings with imputation weights to adaptively complete missing node attributes. We then enhance node embeddings with label information to provide supervision for completed nodes, making nodes of the same category distribute consistently across graph domains. Furthermore, we introduce the complement confidence score to dynamically adjust the scope of trustworthy embeddings, so as to improve the quality of subsequent cross-graph imputation. Extensive experiments on a range of cross-graph tasks validate the effectiveness of AC-GDA.