用于属性图集群的辅助图
Wang Li1, Siwei Wang1, Xifeng Guo2
1School of Computer, National University of Defense Technology, Changsha 410000, China.
Entropy (Basel, Switzerland)
|July 8, 2023
概括
本研究介绍了辅助图形属性图形集群 (AGAGC) 技术,以改进图形集群. AGAGC通过结合非本地关系来增强节点表示,从而带来了优越的集群性能.
科学领域:
- 计算机科学 计算机科学
- 数据挖掘 数据挖掘
- 机器学习 机器学习
背景情况:
- 属性图集群算法利用拓信息进行稳健的节点表示.
- 现有的方法往往忽略了非直接链接的节点之间的关系,限制了聚类潜力.
研究的目的:
- 引入属性图集群 (AGAGC) 技术的辅助图形.
- 通过捕捉更广泛的节点关系来增强属性图集群.
主要方法:
- 使用噪声过方法,基于节点属性构建辅助图.
- 训练一个由原始和辅助图表监督的集群模型.
- 合并多层嵌入式并使用自主监督的集群模块与三重损失.
主要成果:
- 拟议的AGAGC模型与最先进的图形集群方法相比,显示出具有竞争力或优异的性能.
- 在四个基准数据集上进行的实验验证了AGAGC技术的有效性.
结论:
- 通过结合辅助图形信息,AGAGC有效地解决了现有的属性图形集群的局限性.
- 该方法增强了代表性的歧视力和集群意识,以改善结果.
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