MIRAGE: Mutual Information-guided Reconstruction and Adaptation for Graph Domain Adaptation with Edge Incompleteness

Graph Domain Adaptation (GDA) aims to transfer knowledge from a labeled source graph to an unlabeled target graph. Existing GDA methods typically rely on fully observed graph topologies for representation learning and cross-domain alignment. However, real-world graph topologies can be incomplete, with edges unobserved due to corruption, adversarial perturbations, or privacy constraints. In this paper, we formulate this scenario as *Edge-Incomplete Graph Domain Adaptation* (EIGDA), where domain shift coexists with structural incompleteness in source and target graphs. Moreover, we show that EIGDA poses a key challenge: missing edges compromise available neighborhood information and can further exacerbate the source-target performance gap. To address this issue, we propose **MIRAGE** (**M**utual **I**nformation-guided **R**econstruction and **A**daptation for **G**raph domain adaptation with **E**dge incompleteness), a novel algorithm that leverages mutual information maximization to guide transfer-aware edge selection, thereby allowing graph reconstruction to preserve local information while supporting cross-domain adaptation. Experiments on multiple benchmark datasets with varying missing-edge ratios show that MIRAGE consistently outperforms competitive baselines under structurally incomplete EIGDA settings.