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图形变压器具有高度节点定用于图形分区
Zhengxi Yang1, Lingfeng Niu2, Minglong Lei3
1School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China.
本研究介绍了一种新的图形转换器网络用于图形分区,有效地结合了本地和全球图形结构. 拟议的方法通过使用节点来提高分区准确性,并优于现有的启发式和深度学习方法.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 图的分区对于将节点分为平衡子集至关重要.
- 图形神经网络 (GNN) 越来越多地使用,但在全球平衡和节点平滑方面存在困难.
- 现有的GNN主要集中在局部图形结构上,忽视了基本的全球背景.
研究的目的:
- 开发一种改进的图形分区方法,解决当前GNN的局限性.
- 整合本地和全球图形信息,以获得卓越的分区性能.
- 为了利用高度节点作为点来实现更准确,更平衡的分区.
主要方法:
- 提出了一个局部-全球图形变压器网络,计算连接节点和全球虚拟节点之间的注意力.
- 高度节点被确定为重要,并被用作,通过传统方法分配标签.
- 一个半监督的框架结合了传统的图形分区损失与高度节点定的监督损失.
主要成果:
- 拟议的图形变压器网络有效地结合了本地和全球图形结构.
- 该方法在图形分区中显示出优越的平衡特性.
- 实验结果显示,该模型在关键分区指标上优于启发式和现有的深度学习方法.
结论:
- 具有节点的新型图形变压器网络在图形分区方面取得了重大进展.
- 整合本地-全球关注和高度节点定,可以提高分区的准确性和平衡性.
- 这种方法为复杂的图形分区任务提供了现有方法的强有力的替代方案.
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