A curriculum is a planned sequence of learning materials and an effective one can make learning efficient and effective for both humans and machines. Recent studies developed effective data-driven curriculum learning approaches for training graph neural networks in language applications. However, existing curriculum learning approaches often employ a single criterion of difficulty in their training paradigms. In this paper, we propose a new perspective on curriculum learning by introducing a novel approach that builds on graph complexity formalisms (as difficulty criteria) and model competence during training. The model consists of a scheduling scheme which derives effective curricula by accounting for different views of sample difficulty and model competence during training. The proposed solution advances existing research in curriculum learning for graph neural networks with the ability to incorporate a fine-grained spectrum of graph difficulty criteria in their training paradigms. Experimental results on real-world link prediction and node classification tasks illustrate the effectiveness of the proposed approach.

翻译：课程是指对学习材料进行有计划的序列安排，有效的课程能够使人类和机器的学习过程更加高效且有效。近期的研究开发了基于数据驱动的课程学习方法，用于在语言应用中训练图神经网络。然而，现有课程学习方法在训练范式中通常采用单一的难度标准。本文提出了一种新的课程学习视角，引入了一种基于图复杂度形式化（作为难度标准）与模型训练过程中能力的新方法。该模型包含一种调度机制，通过考量样本难度的不同视角以及训练过程中的模型能力，推导出有效的课程安排。所提出的方案使面向图神经网络的课程学习研究取得进展，能够在训练范式中融入细粒度的图难度标准谱系。在真实世界链接预测与节点分类任务上的实验结果证明了该方法的有效性。