对比图形自动编码器用于图形嵌入
Shuaishuai Zu1, Li Li1, Jun Shen2
1School of Computer and Information Science, Southwest University, China.
概括
本研究介绍了对比图自编码器 (CGAE) 和对比变量图自编码器 (CVGAE),以改善图对比学习 (GCL) 中的节点嵌入. 这些方法通过保留语义信息和避免负样本问题来增强概括性.
科学领域:
- 机器学习 机器学习
- 图形神经网络 图形神经网络
- 代表性学习学习学习
背景情况:
- 图形嵌入方法往往难以平衡结构和特征信息,限制了概括.
- 图形对比学习 (GCL) 是有前途的,但在学习歧视性节点嵌入方面面临挑战.
- 现有的GCL方法可以通过数据增强和不正确的负采样来降低语义信息.
研究的目的:
- 为了解决现有的GCL方法对节点嵌入的局限性.
- 提出新的方法来保护语义信息,并改进负采样策略.
- 为了提高节点嵌入的区分能力,用于下游任务.
主要方法:
- 引入对比图形自编码器 (CGAE) 和对比变量图形自编码器 (CVGAE).
- 为平行编码器开发分布依赖的规范化,以生成对比的表示.
- 利用截断的三位数损失来改进负样本选择,防止聚类节点过度分离.
主要成果:
- 拟议的方法,CGAE和CVGAE,显示了比基线方法更好的性能.
- 实验结果显示了链接预测,节点聚类和图形可视化任务的进步.
- 理论分析支持开发模型的有效性和稳定性.
结论:
- 在GCL中,CGAE和CVGAE有效地克服了语义信息丢失和负采样的挑战.
- 拟议的规范化和采样策略导致了更具歧视性的节点嵌入.
- 这些模型为各种基于图形的机器学习任务提供了显著的改进.
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