基于变量图自动编码器的节点集群的多层次对比学习
IEEE transactions on neural networks and learning systems
|October 31, 2025
概括
我们介绍了一种新型的多尺度对比变量图自动编码器 (MCVGAE),以增强节点集群. MCVGAE解决了现有模型中的关键挑战,显著提高了聚类准确性和表示学习.
科学领域:
- 机器学习 机器学习
- 图形神经网络 图形神经网络
- 没有监督的学习学习.
背景情况:
- 变量图自动编码器 (VGAEs) 被广泛用于节点集群.
- 现有的VGAE遭受后部崩 (PC),特征随机性 (FR) 和特征漂移 (FD) 等问题.
- 这些挑战源于推理/生成模型的不匹配以及杂的集群分配.
研究的目的:
- 提出一种新的多尺度对比变量图自动编码器 (MCVGAE),以克服当前VGAE节点集群的局限性.
- 为了改善隐藏表示和数据分布之间的对齐,防止PC.
- 为了有效地减少FR和FD,以提高集群性能.
主要方法:
- MCVGAE集成了集群级和图表级的对比学习.
- 它采用了近距离层面和集群层面的自我监督学习策略.
- 多尺度方法提高了表示学习和聚类准确性.
主要成果:
- 在多个基准数据集 (Cora,ACM,Pubmed,Citeseer,DBLP,Wiki) 中,MCVGAE表现出卓越的性能.
- 获得了高准确度分数,例如,Cora上的79.09%和ACM上的90.04%.
- 超越了30个最先进的节点聚类方法.
结论:
- MCVGAE有效地解决了基于VGAE的节点集群中的关键挑战.
- 拟议的模型提供了改进的隐性空间对齐和强大的特征表示.
- MCVGAE代表了对图表表示学习用于集群任务的重大进步.
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