BiKT:通过双向知识转移释放 GNN 的潜力
IEEE transactions on pattern analysis and machine intelligence
|November 24, 2025
概括
我们介绍了双向知识传输 (BiKT),这是一种增强图形神经网络 (GNN) 的新方法. BiKT优化了特征转换,提高了GNN的性能,并允许灵活地应用衍生模型.
科学领域:
- 图形神经网络 (GNN) 是一个神经网络.
- 代表性学习学习学习
- 机器学习 机器学习
背景情况:
- 消息传递范式是GNN的关键,重点是特征传播.
- 在GNN中特征转换尚未得到充分探索.
- 不过,GNN可能无法充分利用固有的特征转换能力.
研究的目的:
- 研究GNN特征转换性能.研究GNN特征转换性能.
- 提出双向知识转移 (BiKT) 的建议,以加强GNN.
- 释放特征转换操作的潜力.
主要方法:
- 在GNN中特征转换的实证研究.
- 开发BiKT作为一个插即用方法.
- 衍生表示学习模型与原始GNN共享参数.
- 在GNN和衍生模型之间进行双向知识注入.
主要成果:
- 在7个数据集和5个GNN中,BiKT将GNN性能提高0.5%-4%.
- 衍生模型显示出与原来的GNN相比具有竞争力或更高的性能.
- 理论分析证实BiKT通过域调整增强了概括界限.
结论:
- 通过优化特征转换,BiKT有效地提高了GNN的性能.
- 衍生模型为下游任务提供了一个强大的,独立适用的工具.
- BiKT为GNN提供了一个灵活的,与架构无关的增强功能.
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