一个端到端的双目标方法来深度图表分区
Pengcheng Wei1, Yuan Fang2, Zhihao Wen2
1Information Systems Technology and Design Pillar, Singapore University of Technology and Design, 485998, Singapore.
概括
我们开发了一种新的图形神经网络 (GNN) 方法来进行图形分区. 这种方法平衡了分区大小,并最大限度地减少了图形切割,提高了大规模网络的可扩展性和质量.
科学领域:
- 计算机科学 计算机科学
- 图形理论 图形理论
- 机器学习 机器学习
背景情况:
- 图形分区对于像社交网络和计算图形这样的大规模网络至关重要.
- 双目标问题旨在尽量减少边缘切割和平衡隔断尺寸.
- 现有的方法在可扩展性或分区平衡方面扎,深度学习方法缺乏端到端优化.
研究的目的:
- 引入一种使用图形神经网络 (GNN) 的新型,端到端的图形分区方法.
- 通过最小化切割和确保分区平衡来解决双目标图的分区问题.
- 为了利用多层图形功能来提高分区性能.
主要方法:
- 使用的图形神经网络 (GNN) 具有多层图形特征 (节点,本地,全球).
- 制定了一个有限的双目标函数,以最大限度地减少图形切割和平衡分区大小.
- 开发了一个基于GNN的深度模型,使用Hardmax运算符进行端到端优化.
主要成果:
- 提出的GNN方法显著提高了分区质量和可扩展性.
- 与现有的双目标和深度图分区基线相比,表现出卓越的性能.
- 在各种应用和规模的12个不同数据集上进行验证.
结论:
- 新的GNN方法为双目标图表分区提供了一个有效的端到端解决方案.
- 这种方法提高了分区的质量和大图的可扩展性.
- 多层特征探索和Hardmax操作员有助于提高性能.
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