T2-GNN: Graph Neural Networks for Graphs with Incomplete Features and Structure via Teacher-Student Distillation

Huo, Cuiying; Jin, Dayong; Li, Yawen; He, Dongxiao; Yang, Yanjie; Wu, Lingfei

doi:10.48550/arxiv.2212.12738

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Knowledge Distillation In Molecular Gnns1

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2022

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“…One needs to ensure that paired features are similar both in modeling capacity and relevance to the output. Most research on feature-based distillation on graphs has so far focused on models that only have one type of (scalar) features in single-output classification tasks [26,27,28,29], thereby reducing the problem to the selection of layers to pair across the student and the teacher. This is often further simplified by utilizing models of the same architecture.…”

Section: Knowledge Distillation In Molecular Gnnsmentioning

confidence: 99%