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DWSSA:缓解深度图形神经网络的过度平滑性
Qirong Zhang1, Jin Li1, Qingqing Ye2
1College of Computer and Data Science, Fuzhou University, Fuzhou 350116, PR China.
概括
本研究介绍了一种具有结构增强 (DWSSA) 的动态权重策略,用于对抗深度图形神经网络 (GNN) 的过度平滑性. DWSSA通过动态调整节点聚合权重和增强图形结构来提高GNN性能.
科学领域:
- 机器学习 机器学习
- 图形神经网络的神经网络
背景情况:
- 深度图形神经网络 (GNN) 在图形任务中表现出色,但由于浅的网络和均的重量聚合,它们面临过度平滑.
- 过度平滑性限制了知识捕获,并通过过度强调节点特征和静态结构,阻碍了深度GNN中的性能.
研究的目的:
- 提出一个新的动态权重策略与结构增强 (DWSSA),以减轻深度GNN的过度平滑.
- 通过解决知识捕获和图形结构利用的局限性来提高GNN的性能.
主要方法:
- 实现了用于节点集群和模糊赋值的模糊C-Means (FCM).
- 在聚合中设计了一种用于动态重量调整的新型度量函数.
- 引入了一个结构增强 (SA) 步骤,使用并行KNN算法来添加有意义的连接.
主要成果:
- 拟议的DWSSA方法有效地减轻了深度GNN中的过度光滑.
- DWSSA在11个同型和异型图标基准中提高了GNN的性能.
- 该策略减少了杂的聚合,并更好地利用拓信息.
结论:
- DWSSA为深层GNN中的过度光滑性问题提供了一个强大的解决方案.
- 该方法提高了GNN在各种图形相关任务中的有效性.
- DWSSA展示了在图表上推进深度学习的巨大潜力.
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