Knowledge-Embedded Hypergraph Neural Networks.

Abstract

Hypergraph Neural Networks (HGNNs) enhance graph-based modeling by representing complex relationships, with applications in brain network analysis, recommendation systems, and computer vision. However, conventional HGNNs often struggle with effective knowledge extraction and discriminative feature representation, leading to performance limitations. This paper presents Knowledge-Embedded Hypergraph Neural Networks (Knowledge HGNN), a framework that addresses these challenges with two complementary encoders and a multi-dimensional fusion strategy. The High-Order Incidence Encoder (HOI-Encoder) explicitly embeds structural knowledge by capturing permutation-invariant high-order incidence patterns that are typically overlooked by standard HGNNs. In contrast, the Task-Driven Rule Encoder (TDR-Encoder) focuses on feature-level knowledge, extracting task-related rules from vertex attributes through gradient boosted decision tree pre-training and encoding both rule content and positional importance. A Multi-Dimensional Knowledge Fusion module then integrates structural and rule-based embeddings, bridging semantic and dimensional gaps to form enriched vertex representations. The framework includes two implementations: Rule-Driven HGNN, which emphasizes rule-based knowledge, and Dual-Driven HGNN, which jointly leverages structural and rule-based knowledge for comprehensive feature extraction. Extensive experiments on ten datasets, together with ablation studies, demonstrate that Knowledge HGNN significantly improves performance, achieving a 7.3% gain on the Cora dataset and an average improvement of 2.5% across all datasets. These results highlight the effectiveness of explicitly differentiating and fusing structural and rule-based knowledge, setting a new standard for hypergraph applications in complex, data-driven scenarios.