一个扰乱恢复生成自编码器,用于缺少属性异质图的异质图
Quan Wang1, Xinru Shao2, Xiaodi Huang3
1Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, 321000, China.
Scientific reports
|March 15, 2026
概括
异质图形生成自编码器 (HGGAE) 通过将缺失作为扰动进行建模来解决缺失节点属性. 这种方法改善了对异质图的表示学习和下游任务性能.
科学领域:
- 图表 机器学习 机器学习
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 异质图对于社交网络,推和生物信息学至关重要.
- 缺失或损坏的节点属性降低了图表表示质量和任务性能.
- 现有的方法在与属性不确定性和复杂的多关系依赖性作斗争.
研究的目的:
- 提出HGGAE (异构图生成自编码器),一种新的生成自编码器框架.
- 解决异质图中缺失和损坏属性的挑战.
- 改善代表性学习和下游任务执行.
主要方法:
- 对于属性恢复,HGGAE采用了一个扰乱恢复范式.
- 它使用可调度的噪声发生器和关系特定的结构性扰动模块.
- 适应性扰动强度和稀疏目标提高训练效率.
主要成果:
- 在基准数据集上,HGGAE在节点分类方面表现强.
- 在IMDB数据集上取得了显著的宏观F1和微型F1收益 (高达7.8%和8.5%).
- 在Yelp,ACM和DBLP数据集上展示了竞争或优异的结果.
结论:
- HGGAE有效地模拟了缺失的属性,并改善了表示学习.
- 该框架在缺少属性的场景中表现出强度和概括能力.
- HGGAE为现实世界异质图形分析提供了一个有前途的解决方案.
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