CGMAE: Self-supervised Masked Auto-Encoder with Cross-Graph node alignment for node classification

Masked Auto-Encoder (MAE) is widely adopted for node classification by recovering the randomly masked graph structure or node attributes. However, traditional MAE methods face two critical challenges: (1) features learned for reconstruction may not align with the downstream classification task, and (2) masking edges risks distorting inherent semantic relationships, degrading representation quality. To overcome these limitations, we propose a simple yet effective self-supervised M asked A uto- E ncoder with C ross- G raph node alignment (CGMAE) for node classification. It leverages labeled nodes from an auxiliary graph to enhance discriminative feature learning in an unlabeled target graph, bridging the task gap between reconstruction and classification. CGMAE introduces a node-level alignment mechanism to address distribution shifts across graphs. This design jointly learns structural patterns and node attributes through specific encoders, enabling multi-view feature matching to refine node representations. Furthermore, CGMAE innovatively predicts masked target edges using aligned nodes from the auxiliary graph, preserving the semantic relationships during reconstruction. Extensive experiments on six diverse networks (standard, complex, sparse, and large-scale graphs) verify the effectiveness and robustness of the proposed method in self-supervised/unsupervised node classification tasks, with accuracy improvements ranging from 1.5%/1.1% to 3.5%/7.0% over state-of-the-art methods. Code is available at https://github.com/songruoxian/CGMAE . • A self-supervised paradigm unifies reconstruction and discriminative learning. • A graph MAE with CGNA bridges the reconstruction-classification gap. • Dual-branch encoders reduce graph structural noise. • Experiments on six datasets show strong node classification results.