使用自适应编码器进行对比的多视图属性图表集群.
IEEE transactions on neural networks and learning systems
|April 29, 2024
概括
本研究引入了一种新的对比的多视图属性图集群 (CMAGC) 方法. 通过发现固有的关系和处理稀疏的图形结构来改善节点集群,CMAGC有效地解决了现有方法的局限性.
科学领域:
- 图形理论是指图形的理论.
- 机器学习是机器学习.
- 数据挖掘是一种数据挖掘.
背景情况:
- 多视图属性图集群利用拓结构和节点属性.
- 现有的方法与固有的多视图图关系和稀疏数据作斗争.
- 需要适应不同图形属性的方法.
研究的目的:
- 提出一种新的对比的多视图属性图集群 (CMAGC) 方法.
- 为了明确地发现内在和多视图图表之间的内在关系.
- 在图形嵌入学习中有效处理稀疏的图形结构.
主要方法:
- 使用自适应编码器,根据图形属性 (例如,高阶邻居) 选择GCN编码器层.
- 功能级别和集群级别的对比学习应用于多视图软任务.
- 数据增强使用一级邻居的结合作为正对来解决稀疏性.
主要成果:
- 拟议的CMAGC方法在最先进的技术上显示出优越性.
- 这种方法有效地整合了多视图属性图信息.
- 处理稀疏的图形结构,并发现inter/intra-view关系.
结论:
- 在多视图属性图表集群中,CMAGC提供了显著的进步.
- 该方法为处理各种图形属性和稀疏性提供了一个强大的框架.
- 这项工作是第一个明确地从inter-和intra-view视角解决固有的关系.
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