Nonparametric Teaching for Graph Property Learners

• URL: http://arxiv.org/abs/2505.14170v2
• Date: Wed, 21 May 2025 07:09:21 GMT
• Title: Nonparametric Teaching for Graph Property Learners
• Authors: Chen Zhang, Weixin Bu, Zeyi Ren, Zhengwu Liu, Yik-Chung Wu, Ngai Wong,
• Abstract summary: We propose a paradigm called Graph Neural Teaching (GraNT) that reinterprets the learning process through a novel nonparametric teaching perspective.<n>GraNT offers a theoretical framework for teaching implicitly defined (i.e., nonparametric) mappings via example selection.<n>We show for the first time that teaching graph property learners is consistent with teaching structure-aware nonparametric learners.
• Score: 21.96981353343662
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inferring properties of graph-structured data, e.g., the solubility of molecules, essentially involves learning the implicit mapping from graphs to their properties. This learning process is often costly for graph property learners like Graph Convolutional Networks (GCNs). To address this, we propose a paradigm called Graph Neural Teaching (GraNT) that reinterprets the learning process through a novel nonparametric teaching perspective. Specifically, the latter offers a theoretical framework for teaching implicitly defined (i.e., nonparametric) mappings via example selection. Such an implicit mapping is realized by a dense set of graph-property pairs, with the GraNT teacher selecting a subset of them to promote faster convergence in GCN training. By analytically examining the impact of graph structure on parameter-based gradient descent during training, and recasting the evolution of GCNs--shaped by parameter updates--through functional gradient descent in nonparametric teaching, we show for the first time that teaching graph property learners (i.e., GCNs) is consistent with teaching structure-aware nonparametric learners. These new findings readily commit GraNT to enhancing learning efficiency of the graph property learner, showing significant reductions in training time for graph-level regression (-36.62%), graph-level classification (-38.19%), node-level regression (-30.97%) and node-level classification (-47.30%), all while maintaining its generalization performance.

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