MoGL:一个混合的不同质的专家协作图表学习的混合
Gonghai Zhou1, Zhiwei Xu2, Kaixuan Yang1
1College of Computer Science, Nankai University, Tianjin, Tianjin, 300350, China.
概括
混合异质专家协作图形学习 (MoGL) 通过整合全球和当地专家来增强图形神经网络 (GNN). 这种新的方法可以提高图形学习的准确性和效率.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 图形表示学习学习学习图形表示学习
背景情况:
- 图形神经网络 (GNN) 很出色,但与局部图形异质性作斗争.
- 现有的专家混合 (MoE) 模型面临由于门关机制的准确性-效率权衡.
研究的目的:
- 引入异质专家组合的协作图形学习 (MoGL),以解决GNN的局限性.
- 开发一个新的框架,将全球和本地专家结合起来,以实现卓越的图形学习.
主要方法:
- 提出一个双专家架构:用于全球结构的标准GNN和用于本地模式的图形Kolmogorov-Arnold同态网络 (GKAIN).
- 实施基于信任的门禁机制,以实现动态的专家权重分配.
- 利用一个协作式的培训范式与统一的损失函数协同知识传输.
主要成果:
- 对于均质和异质图表,MoGL在各种基准上取得了最先进的表现.
- 与现有方法相比,显示出更高的预测准确性和计算效率.
- 验证了协同作用的双专家架构和基于信任的门的有效性.
结论:
- 通过有效地捕捉当地异质性,MoGL在图形学习中提供了显著的进步.
- 拟议的框架为复杂的图形数据提供了可扩展和高效的解决方案.
- MoGL代表了开发强大且多功能图表表示学习模型的新方向.
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