Search to integrate multi-level heuristics with graph neural networks for multi-relational link prediction

作者：Junjie Wu, Haotong Du, Haowei Xu, Xianghua Li, Chao Gao, Zhen Wang · 发表于：Neurocomputing · 年份：2025 · DOI：10.1016/j.neucom.2025.130776 · 被引用次数：9 · 研究领域：Advanced Graph Neural Networks、Complex Network Analysis Techniques、Bioinformatics and Genomic Networks

Multi-relational link prediction aims to forecast relationships among nodes within multi-relational graphs, with applications ranging from predicting drug interactions to completing knowledge graphs. Recent advancements have demonstrated that graph neural networks (GNNs), when augmented with heuristic information, significantly enhance the performance in this domain. However, existing approaches are limited by their uniform application of a single heuristic level across various datasets and often overlook the synergy between heuristic information and GNN architecture. Inspired by the successes of neural architecture search (NAS), this paper proposes a novel strategy that seeks to integrate multi-level heuristics with GNNs for enhanced multi-relational link prediction. This strategy involves a new framework that incorporates heuristic information at both global and local levels into GNNs, facilitating a cohesive application of these heuristics within the architecture. Building upon this framework, we have developed an extensive search space that includes widely-used heuristics and operations prevalent in manually designed architectures. Moreover, we employ a versatile search algorithm tailored to tackle the bi-level optimization challenge, ensuring efficient exploration of the search space. Empirical evaluations conducted on four benchmark datasets demonstrate that the proposed method significantly surpasses existing baselines in the task of multi-relational link prediction.