Flow2GNN:灵活的双向流程信息传递用于增强GNN超越同性恋
IEEE transactions on cybernetics
|July 10, 2024
概括
在消息传递 (MP) 中解脱局部社区,可以在异构图上改进图形神经网络 (GNN). Flow2GNN通过重新分配异构信息来增强GNN,从而提高具有挑战性的数据集的性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 图形神经网络的神经网络
背景情况:
- 信息传递 (MP) 是图形神经网络 (GNN) 的基础.
- 传统的MP因为信息多余而难以处理异构图.
- 现有的以异构为重点的GNN通常具有复杂的设计.
研究的目的:
- 提出一个新的,简单的消息传递方案,Flow2GNN,以提高在异构图上的GNN性能.
- 通过解和重新分配拓和属性空间中的异构信息来增强GNN.
- 证明拟议方法在改进各种GNN架构方面的灵活性.
主要方法:
- 介绍了Flow2GNN,一个双向消息传递神经网络.
- 开发了一个脱的运算符来分离输入流和输出流的拓信息.
- 采用适应性聚合模型来调整同型和异型属性流.
- 提供了理论证明,解减少了概括差距.
主要成果:
- 在异构图上,Flow2GNN的表现优于现有的最先进的GNN.
- 该方法显著提高了GCN,GAT,GCNII和H2GCN的性能.
- 在威斯康星州数据集上,GCN的性能提高了25.88%.
- 通过脱而出的消息传递来证明了增强的GNN功能.
结论:
- 拟议的Flow2GNN提供了一种简单而有效的方法来处理GNN中的异构性.
- 解消息传递是一种可行的策略,可以改善GNN对异构数据的概括.
- Flow2GNN提供了一个灵活的框架,可以增强现有的GNN模型.
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