深度图形神经网络的自动设计与解模式
IEEE transactions on neural networks and learning systems
|August 14, 2024
概括
本研究介绍了一种新的神经架构搜索 (NAS) 方法,用于自动设计深度图形神经网络 (GNN). 该方法有效地平衡了节点分类任务的准确性和计算成本.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 图形神经网络的神经网络
背景情况:
- 图形神经网络 (GNN) 对于图形数据上的节点分类是有效的.
- 浅的GNN具有局限性,这导致了深度GNN的研究.
- 由于过度平滑等问题,深度GNN的手动设计具有挑战性.
研究的目的:
- 为自动深度GNN设计提出一种新的神经架构搜索 (NAS) 方法.
- 为了解决当前GNN架构的局限性,寻找深度网络.
- 在大规模图形数据上增强节点分类性能.
主要方法:
- 使用脱的传播和转换模式重新设计了GNN搜索空间.
- 制定了架构搜索作为一个多目标优化问题.
- 平衡的准确性和计算效率.
主要成果:
- 拟议的NAS方法成功地自动设计深度GNN.
- 在基准数据集的各种节点分类任务上取得了强的表现.
- 在大规模图形数据集上展示了可扩展性.
结论:
- 这种新的NAS方法有效地自动化了深度GNN设计.
- 该方法克服了与手动深度GNN设计相关的挑战.
- 该方法具有可扩展性,并且在各种节点分类应用中表现良好.
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