HIRE: Distilling High-order Relational Knowledge From Heterogeneous Graph Neural Networks

• URL: http://arxiv.org/abs/2207.11887v1
• Date: Mon, 25 Jul 2022 03:25:15 GMT
• Title: HIRE: Distilling High-order Relational Knowledge From Heterogeneous Graph Neural Networks
• Authors: Jing Liu, Tongya Zheng, and Qinfen Hao
• Abstract summary: We propose 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.
• Score: 4.713436329217004
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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