This paper studies the problem of CPRP, concept prerequisite relation prediction, which is a fundamental task in using AI for education. CPRP is usually formulated into a link-prediction task on a relationship graph of concepts and solved by training the graph neural network (GNN) model. However, current directed GNNs fail to manage graph isomorphism which refers to the invariance of non-isomorphic graphs, reducing the expressivity of resulting representations. We present a permutation-equivariant directed GNN model by introducing the Weisfeiler-Lehman test into directed GNN learning. Our method is then used for CPRP and evaluated on three public datasets. The experimental results show that our model delivers better prediction performance than the state-of-the-art methods.

翻译：本文研究概念前置关系预测（CPRP）问题，这是人工智能应用于教育领域的基础性任务。通常将CPRP形式化为概念关系图上的链接预测任务，并通过训练图神经网络模型求解。然而，现有有向图神经网络未能有效应对图同构问题——即无法保持非同构图的差异性，导致表征表达能力受限。本文通过将Weisfeiler-Lehman测试引入有向图学习，提出一种置换等变的有向图神经网络模型。该方法随后被应用于CPRP任务，并在三个公开数据集上进行了评估。实验结果表明，本模型相比现有最优方法具有更优的预测性能。