通过对齐表示学习进行深度图形集群
Zhikui Chen1, Lifang Li1, Xu Zhang1
1DUT School of Software Technology and DUT-RU International School of Information Science and Engineering, Dalian University of Technology, TuQiang 321 street, Development Zone, Dalian, 116620, Liaoning, China.
概括
调整表示学习网络 (ARLN) 通过使用自动编码器之间的对比学习来改进深度图集群,以创建更具歧视性的节点表示. 这种新的方法提高了集群性能,而不依赖于复杂的数据增强.
科学领域:
- 图形神经网络的神经网络
- 机器学习 机器学习
- 数据挖掘 数据挖掘
背景情况:
- 深度图表集群对于分析图形结构数据至关重要.
- 现有的自编码器和图形卷积网络方法通常会产生非歧视性的节点表示.
- 目前的对比图集群方法受到依赖数据增强和缺乏自我一致性的限制.
研究的目的:
- 提出一种新的对比的深度图集群方法,即对齐表示学习网络 (ARLN).
- 为了提高节点表示的可区分性和集群性能.
- 解决现有方法在数据增强和自我一致性方面的局限性.
主要方法:
- 使用自编码器和图形自编码器之间的对比学习来绕过复杂的数据增强.
- 为共识表示学习引入实例和特征对比模块.
- 设计一个赋值概率对比模块,以确保节点表示和集群赋值之间的自我一致性.
主要成果:
- 通过对比学习,ARLN学习了歧视性节点表示.
- 该方法在节点表示和集群赋值之间保持了自我一致性.
- 实验结果表明ARLN在基准数据集上优于最先进的深度图集群方法.
结论:
- 通过利用对比式学习,ARLN提供了一种有效的深度图集群方法.
- 拟议的方法可以提高表示学习和聚类准确性.
- ARLN为现有方法提供了强大的替代方案,特别是那些依赖于数据增强的方法.
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