在带有隐性链接类型异质性的图形上进行联合节点分类
概括
联合学习 (FL) 通过在图表中发现隐藏的链接类型来解决数据异质性. FedLit框架有效地模拟了这些多样化的链接类型之间的消息传递,以提高性能.
科学领域:
- 机器学习 机器学习
- 图形神经网络 图形神经网络
- 联邦学习学习 (Federated Learning) 是一种学习方式.
背景情况:
- 联合学习 (FL) 从去中心化数据中训练全球模型,但非IID (非独立和相同分布) 数据带来了挑战.
- 图形数据经常表现出链接类型的异质性,其中链接具有不同的语义和同类性,在客户端上有所不同.
研究的目的:
- 提出一个新的图形FL框架,同时发现潜在的链接类型和模型链接特定的消息传递.
- 为了应对图形联合学习中链接类型异质性的挑战.
主要方法:
- 开发了FedLit,这是一个用于图形的联合学习框架.
- 采用基于EM的集群算法来进行动态隐藏链路类型检测.
- 利用多个卷积通道来区分基于发现的链接类型的消息传递.
主要成果:
- 合成了现实的图形数据集,具有潜在的异质链接类型.
- 分区数据集以模拟不同级别的链接类型异质性.
- 通过全面的实验证明了FedLit框架的卓越性能和合理行为.
结论:
- 在图形联合学习中,FedLit有效地处理链接类型异质性.
- 该框架显示了改善复杂,现实世界的图形数据的FL性能的前景.
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