Scholarlink

<p>Heterogeneous graph data in real world is characterized by a wide variety of node and edge types and complex structures. They also commonly suffer from label scarcity and incomplete data, which constrains the performance and generalization capabilities of traditional semi-supervised graph neural networks. Contrastive learning methods based on multi-graph paradigm can achieve excellent performance in label-scarce scenarios. However, many of these approaches enhance each meta-path-based view from a local perspective, struggling to fully exploit semantic and structural information. Moreover, existing methods often employ batch normalization to reduce redundancy, yet the normalized features can retain correlation, potentially leading to dimensional collapse. To address these challenges, we propose the Heterogeneous Graph Contrastive Learning with Meta-path Augmentation and Whitening (HGCL-MAW). Based on a self-supervised contrastive learning framework, HGCL-MAW captures comprehensive graph information by designing and constructing meta-path-based graph augmentation and neighbor augmentation strategies to learn more robust node representations. Meanwhile, an encoder based on the graph attention technique is employed, which performs intra-graph aggregation to capture local structural features and inter-graph aggregation to fuse information from different meta-paths, thereby capturing global semantic features. Furthermore, a whitening technique is introduced to process node embeddings, which effectively reduces dimensional correlation, preserves feature independence, and alleviates the issue of dimensional collapse. Experimental results on four public datasets for downstream tasks, including node classification and clustering, demonstrate that the proposed model significantly improves performance under label-scarce conditions, thus validating the effectiveness of our method. Our code and data are available at <a href="https://github.com/desslie047/HGCL-MAW">https://github.com/desslie047/HGCL-MAW</a>.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

HGCL-MAW: heterogeneous graph contrastive learning with meta-path augmentation and whitening

  • 期刊论文

  • 发表日期：

    2026年7月11日

查看全文
  • Ya Liu,
  • Ningyuan Guo,
  • Chao Li,
  • Ge Song,
  • Chunmei Jiang,
  • Qingtian Zeng

摘要

Heterogeneous graph data in real world is characterized by a wide variety of node and edge types and complex structures. They also commonly suffer from label scarcity and incomplete data, which constrains the performance and generalization capabilities of traditional semi-supervised graph neural networks. Contrastive learning methods based on multi-graph paradigm can achieve excellent performance in label-scarce scenarios. However, many of these approaches enhance each meta-path-based view from a local perspective, struggling to fully exploit semantic and structural information. Moreover, existing methods often employ batch normalization to reduce redundancy, yet the normalized features can retain correlation, potentially leading to dimensional collapse. To address these challenges, we propose the Heterogeneous Graph Contrastive Learning with Meta-path Augmentation and Whitening (HGCL-MAW). Based on a self-supervised contrastive learning framework, HGCL-MAW captures comprehensive graph information by designing and constructing meta-path-based graph augmentation and neighbor augmentation strategies to learn more robust node representations. Meanwhile, an encoder based on the graph attention technique is employed, which performs intra-graph aggregation to capture local structural features and inter-graph aggregation to fuse information from different meta-paths, thereby capturing global semantic features. Furthermore, a whitening technique is introduced to process node embeddings, which effectively reduces dimensional correlation, preserves feature independence, and alleviates the issue of dimensional collapse. Experimental results on four public datasets for downstream tasks, including node classification and clustering, demonstrate that the proposed model significantly improves performance under label-scarce conditions, thus validating the effectiveness of our method. Our code and data are available at https://github.com/desslie047/HGCL-MAW.