适应感受场图形神经网络的自适应神经网络.
Hepeng Gao1, Funing Yang1, Yongjian Yang1
1Jilin University, Changchun, 130012, Jilin, China.
概括
图形神经网络 (GNN) 由于过度平滑而面临性能下降. 我们的自适应受体场GNN (ADRP-GNN) 通过自适应扩展受体场来减轻这种情况,从而提高节点分类的准确性.
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) 是对表示学习的强大工具,但由于过度平滑问题,它们在更深层次的架构中遭受性能退化.
- 过度平滑导致节点表示变得无法区分,限制了深度GNN的有效性.
研究的目的:
- 为了解决深度GNN中的过度平滑问题.
- 提出一种新的GNN架构,以增加深度保持性能.
- 通过自适应地聚合邻居信息来提高节点分类的准确性.
主要方法:
- 引入了一个自适应感应场图神经网络 (ADRP-GNN),该网络使用单层图形卷积层.
- 开发了一种多节点图形卷积网络 (MuGC),以在单一层中捕获多节点邻近信息.
- 整合了一个MetaLearner用于自适应的接收场生成和一个骨干网络来增强学习能力.
主要成果:
- 拟议的ADRP-GNN有效地减轻了过度平滑的问题,而不需要更深的网络.
- 在八个数据集的实验中,与最先进的方法相比,在节点分类任务中,准确度的提高从0.52%到6.88%不等.
- 适应式接收场机制允许与现有的GNN框架集成,用于各种应用.
结论:
- ADRP-GNN为GNN中的过度平滑问题提供了一个可行的解决方案.
- 邻居信息的自适应聚合增强了表示学习和分类性能.
- 这种架构为各种基于GNN的任务提供了灵活和有效的方法.
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