具有双通道标签对齐的结构保存对比图集
Guang-Yu Zhang1, Yan-Di Huang1, Dong Huang1
1College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China.
概括
这项研究引入了一种新的结构保存对比图集群 (SCGC-DLA) 方法. 它通过保留结构和对齐双通道标签来增强集群,克服了以前对比图集技术的限制.
科学领域:
- 图形神经网络
- 机器学习
- 数据挖掘
背景情况:
- 对比图集群 (CGC) 已经迅速发展,但当前的方法面临着数据增强改变语义和忽视歧视性无监督信息的挑战.
- 现有的方法往往忽视动态结构关系,依赖于静态的社区连接,影响集群性能.
研究的目的:
- 建议采用结构维护的对比图集群方法与双通道标签对齐 (SCGC-DLA).
- 在CGC中解决视图生成,样本配对和动态结构关系学习的局限性.
主要方法:
- 使用低通路和混合图表过器来提供可靠,互补的数据增强视图.
- 使用边缘间中心性 (EBC) 构建结构保存矩阵以捕捉拓关系.
- 采用非主导分类理论进行双通道集群分布,以生成与潜在语义标签一致的高可靠性伪标签.
- 应用自主监督学习方案来指导最终集群的整体网络.
主要成果:
- 拟议的SCGC-DLA方法在五个基准数据集中显示出稳定性和有效性.
- 与几种最先进的方法相比,实现了优越的集群性能.
- 在CGC中验证结构保存和双通道标签对齐的好处.
结论:
- SCGC-DLA有效地克服了对比图集群的关键挑战.
- 该方法对增量,结构保存和标签对齐的新方法导致了更好的集群结果.
- 这项工作在图表集群领域取得了重大进展.
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