对图形神经网络的异构分布传播
Zhuonan Zheng1, Sheng Zhou2, Hongjia Xu1
1College of Computer Science, Zhejiang University, Hangzhou, 310027, China; Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, Zhejiang University, Hangzhou, 310027, China.
概括
本研究介绍了图形神经网络 (GNN) 的异构分布传播 (HDP),以提高异构图的性能. HDP可自适应地划分社区,增强复杂图形数据的表示学习.
科学领域:
- 图形神经网络的神经网络
- 机器学习 机器学习
- 数据挖掘 数据挖掘
背景情况:
- 图形神经网络 (GNN) 通过聚合邻近信息,依赖于同类性假设,在表示学习方面表现出色.
- 现实世界的图形经常违反同类性,需要特殊的方法,如异性图形神经网络 (HeterGNNs).
- 现有的HeterGNN在有效地划分社区和建模异性恋方面面临挑战.
研究的目的:
- 提出一种新的方法,即异构分布传播 (HDP),以解决当前异构GNN中的局限性.
- 为了提高GNN在节点表现出不相似的行为尽管近距离的图形上的性能.
- 改进复杂的,现实世界的图形结构中的表示学习.
主要方法:
- HDP在训练过程中通过伪分配将邻居分为同型和异型组件.
- 一个值得信赖的原型对比学习范式与一个直角面向的约束被用来学习异构邻近分布.
- 一个新的语义意识的信息传递机制传播了同型和异型模式.
主要成果:
- 在9个基准数据集上进行了广泛的实验,这些数据集具有不同水平的同类性.
- 与对异构数据集的代表性基线方法相比,HDP表现优越.
- 提出的方法有效地处理了违反同类性假设的图形.
结论:
- 异构分布传播 (HDP) 为异构环境中的图形神经网络提供了显著的进步.
- 适应性社区分区和语义意识的消息传递是HDP有效性的关键.
- HDP提供了一个强大的框架,用于复杂的,现实世界的图表的表示学习.
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