Concept Prerequisite Relation Prediction by Using
  Permutation-Equivariant Directed Graph Neural Networks
Xiran Qu Xuequn Shang Yupei Zhang
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.