Unsupervised Graph Representation Learning with Complementary View Alignment

2026-07-27

AI Summary

This work addresses the limitations of existing unsupervised graph representation learning methods, which rely on the homophily assumption and thus struggle to model the heterogeneity of structure and attributes in heterophilic graphs, leading to loss of high-frequency information. To overcome this, the authors propose AlignGAE, a novel framework that employs dual encoders to separately capture structural and attribute information, incorporates node positional encoding to approximate neighborhood identity distributions, and introduces a dual reconstruction task—on both edges and attributes—to align complementary views. Notably, AlignGAE is the first to integrate a theoretically grounded neighborhood identity alignment strategy into spectral-aware learning, preserving view diversity while ensuring semantic consistency. Extensive experiments on twelve benchmark datasets demonstrate that AlignGAE achieves up to an 18.7% performance gain in node classification on heterophilic graphs while maintaining state-of-the-art results on homophilic graphs.

Abstract

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.

Problem

• unsupervised graph representation learning
• heterophilous graphs
• homophily bias
• high-frequency components
• graph neural networks

Innovation

• complementary view alignment
• heterophilous graphs
• frequency-aware representation
• dual-encoder architecture
• Neighborhood Identity Distribution