论文标题
雇用：从异质图神经网络中提取高阶关系知识
HIRE: Distilling High-order Relational Knowledge From Heterogeneous Graph Neural Networks
论文作者
论文摘要
由于学术和工业领域的异质图无处不在，研究人员最近提出了许多异质图神经网络（HGNN）。在本文中，我们不再采用更强大的HGNN模型，而是有兴趣设计一个多功能的插件模块，该模块解释了从预先训练的HGNN中提取关系知识。 据我们所知，我们是第一个在异质图上提出高阶（雇用）知识蒸馏框架的人，无论HGNN的模型体系结构如何，它都可以显着提高预测性能。具体而言，我们的雇用框架最初执行一阶节点级知识蒸馏，该蒸馏曲线及其预测逻辑编码了老师HGNN的语义。同时，二阶关系级知识蒸馏模仿了教师HGNN产生的不同类型的节点嵌入之间的关系相关性。 在各种流行的HGNN模型和三个现实世界的异质图上进行了广泛的实验表明，我们的方法获得了一致且相当大的性能提高，证明了其有效性和泛化能力。
Researchers have recently proposed plenty of heterogeneous graph neural networks (HGNNs) due to the ubiquity of heterogeneous graphs in both academic and industrial areas. Instead of pursuing a more powerful HGNN model, in this paper, we are interested in devising a versatile plug-and-play module, which accounts for distilling relational knowledge from pre-trained HGNNs. To the best of our knowledge, we are the first to propose a HIgh-order RElational (HIRE) knowledge distillation framework on heterogeneous graphs, which can significantly boost the prediction performance regardless of model architectures of HGNNs. Concretely, our HIRE framework initially performs first-order node-level knowledge distillation, which encodes the semantics of the teacher HGNN with its prediction logits. Meanwhile, the second-order relation-level knowledge distillation imitates the relational correlation between node embeddings of different types generated by the teacher HGNN. Extensive experiments on various popular HGNNs models and three real-world heterogeneous graphs demonstrate that our method obtains consistent and considerable performance enhancement, proving its effectiveness and generalization ability.
