2024-08-09-qu24a.md

title	booktitle	year	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Concept Prerequisite Relation Prediction by Using Permutation-Equivariant Directed Graph Neural Networks	Proceedings of the 2024 AAAI Conference on Artificial Intelligence	2024	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 isomor- phism 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.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	qu24a	0	Concept Prerequisite Relation Prediction by Using Permutation-Equivariant Directed Graph Neural Networks	39	47	39-47	39	false	Qu, Xiran and Shang, Xuequn and Zhang, Yupei	given family Xiran Qu given family Xuequn Shang given family Yupei Zhang	2024-08-09		Proceedings of the 2024 AAAI Conference on Artificial Intelligence	257	inproceedings	date-parts 2024 8 9	https://raw.githubusercontent.com/mlresearch/v257/main/assets/qu24a/qu24a.pdf