AI 中文总结
研究无监督图表示学习，提出AlignGAE方法，通过互补视图对齐、双编码器架构等技术，解决现有方法同质性偏差问题，在异质图节点分类上性能显著提升，建立频率感知图表示学习新范式。
AI 中文摘要
无监督图表示学习旨在在不依赖标记数据的情况下，通过捕获结构和属性信息来推导有意义的节点嵌入。现有方法如GAEs存在同质性偏差问题，在异质图上性能下降。本文提出AlignGAE，它是MaskGAE的扩展，通过互补视图对齐保留全频谱。框架采用双编码器架构，结合节点位置编码，使用双重建任务，并提出基于理论的NID对齐策略。实验表明AlignGAE在异质图节点分类上比现有方法性能提升达18.7%，建立了频率感知图表示学习新范式。
英文摘要
Unsupervised graph representation learning aims to derive meaningful node embeddings by capturing both structural and attribute information without relying on labeled data. Existing methods, such as GAEs, have demonstrated effectiveness but typically rely on message-passing mechanisms that assume homophily, leading to performance degradation on heterophilous graphs, where connected nodes exhibit dissimilar features. This homophily bias results in the loss of critical high-frequency components that are essential for identifying heterophilous patterns. To address these challenges, we propose \textsc{AlignGAE}, a novel extension of \textit{MaskGAE} that preserves the full frequency spectrum through complementary view alignment. Our framework introduces a dual-encoder architecture that separately processes structural and attribute information, incorporates node positional encoding to approximate Neighborhood Identity Distribution (NID), and employs dual reconstruction tasks for both edges and node attributes. We further propose theoretically grounded NID alignment strategies that ensure semantic consistency across views while preserving their distinct characteristics. Through comprehensive spectral analysis, we demonstrate that \textsc{AlignGAE} achieves optimal representation properties when the alignment loss converges. Extensive experiments across 12 benchmark datasets validate our approach, showing that \textsc{AlignGAE} outperforms state-of-the-art methods by up to 18.7\% on heterophilous graphs in node classification, while maintaining competitive performance on homophilous graphs. Our results establish a new paradigm for frequency-aware graph representation learning.
