Multiscale Weisfeiler-Leman Directed Graph Neural Networks for Prerequisite-Link Prediction

Yupei Zhang, Xiran Qu, Shuhui Liu, Yan Pang, Xuequn Shang

Research output: Contribution to journalArticlepeer-review

Abstract

Prerequisite-link Prediction (PLP) aims to discover the condition relations of a specific event or a concerned variable, which is a fundamental problem in a large number of fields, such as educational data mining. Current studies on PLP usually developed graph neural networks (GNNs) to learn the representations of pairs of nodes. However, these models fail to distinguish non-isomorphic graphs and integrate multiscale structures, leading to the insufficient expressive capability of GNNs. To this end, we in this paper proposed k-dimensional Weisferiler-Leman directed GNNs, dubbed k-WediGNNs, to recognize non-isomorphic graphs via the Weisferiler-Leman algorithm. Furthermore, we integrated the multiscale structures of a directed graph into k-WediGNNs, dubbed multiscale k-WediGNNs, from the bidirected views of in-degree and out-degree. With the Siamese network, the proposed models are extended to address the problem of PLP. Besides, the expressive power is then interpreted via theoretical proofs. The experiments were conducted on four publicly available datasets for concept prerequisite relation prediction (CPRP). The results show that the proposed models achieve better performance than the state-of-the-art approaches, where our multiscale k-WediGNN achieves a new benchmark in the task of CPRP.

Original languageEnglish
Pages (from-to)3556-3569
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume37
Issue number6
DOIs
StatePublished - 2025

Keywords

  • Prerequisite link prediction
  • Weisfeiler-Leman test
  • directed graph neural networks
  • multiscale structure

Fingerprint

Dive into the research topics of 'Multiscale Weisfeiler-Leman Directed Graph Neural Networks for Prerequisite-Link Prediction'. Together they form a unique fingerprint.

Cite this