隐式图形神经网络具有灵活的传播运算符
Yueyang Pi1, Yang Huang1, Yongquan Shi1
1College of Computer and Data Science, Fuzhou University, Fuzhou, 350116, China; Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou, 350116, China.
概括
这项研究介绍了一种新的隐性图形神经网络,可以克服处理动态,异性图形数据的局限性. 灵活的传播运算符适应数据语义和拓学,改善复杂图形结构的性能.
科学领域:
- 图形神经网络的神经网络
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 隐式图形神经网络 (GNN) 擅长捕获高阶节点信息并减少内存使用量.
- 然而,静态拓学限制了它们在异构图结构数据上的有效性.
- 现有的优化启发的方法与明确的GNN结构和层选择作斗争.
研究的目的:
- 提出一个隐式图形神经网络,具有灵活的传播运算符.
- 解决静态拓在处理异构图的局限性.
- 为优化问题开发一个隐式调整网络层的模型.
主要方法:
- 从优化目标函数中推导一个隐性消息传递公式,使用灵活的传播运算符.
- 共同考虑动态数据语义和拓学,以改进图形表示.
- 使用固定点代过程来优化目标函数.
主要成果:
- 拟议的模型表明,与使用静态运算符的方法相比,对异构图的应用性更好.
- 固定点代过程隐式调整网络层,消除了对层数的先前知识的需求.
- 广泛的实验证实了模型的增强性能和优越性.
结论:
- 开发的含有灵活传播运算符的隐性GNN有效地处理异性图形数据.
- 该模型的自适应性和隐性层调整为图形表示学习提供了显著的进步.
- 这种方法为复杂的图形结构数据分析提供了更强大,更灵活的解决方案.
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