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<p>Performing node classification in text-attributed graphs (TAGs) has become a critical area of research. While two-stage methods effectively address scalability by decoupling feature extraction from GNN training, they often suffer from a “structural gap” where global topology is ignored during text encoding, and local aggregation leads to feature oversmoothing. To bridge this gap, we propose DCD-CAWA, a framework that integrates community structures into both feature learning and message passing. Specifically, in the first stage, we employ Decoupled Community Detection (DCD) to generate structural priors. These are integrated with text attributes through an auxiliary multi-task fine-tuning strategy, forcing the language model to capture the correlations between semantic content and community membership. In the second stage, we introduce the Community-Aware Weighted Aggregation (CAWA) module. The workflow of CAWA involves computing dynamic attention weights between nodes and their respective community prototypes, allowing the model to adaptively refine node representations by emphasizing global community contexts. This approach not only eliminates discrepancies between static topology and dynamic features but also effectively mitigates oversmoothing by preserving community-level distinctiveness. Experimental results across multiple datasets demonstrate that DCD-CAWA significantly outperforms state-of-the-art baselines.</p>

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Extending neighborhood aggregation: fine-grained node representation learning for text-attributed graphs under community structures

  • 期刊论文

  • 发表日期：

    2026年2月18日

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  • Yun Wang,
  • Qiang Zhou,
  • Tianhua Ran,
  • Dedong Lu,
  • Yunqi Mi,
  • Naibin He,
  • Xueming Qian,
  • Guoshuai Zhao

摘要

Performing node classification in text-attributed graphs (TAGs) has become a critical area of research. While two-stage methods effectively address scalability by decoupling feature extraction from GNN training, they often suffer from a “structural gap” where global topology is ignored during text encoding, and local aggregation leads to feature oversmoothing. To bridge this gap, we propose DCD-CAWA, a framework that integrates community structures into both feature learning and message passing. Specifically, in the first stage, we employ Decoupled Community Detection (DCD) to generate structural priors. These are integrated with text attributes through an auxiliary multi-task fine-tuning strategy, forcing the language model to capture the correlations between semantic content and community membership. In the second stage, we introduce the Community-Aware Weighted Aggregation (CAWA) module. The workflow of CAWA involves computing dynamic attention weights between nodes and their respective community prototypes, allowing the model to adaptively refine node representations by emphasizing global community contexts. This approach not only eliminates discrepancies between static topology and dynamic features but also effectively mitigates oversmoothing by preserving community-level distinctiveness. Experimental results across multiple datasets demonstrate that DCD-CAWA significantly outperforms state-of-the-art baselines.