BG-HGNN: Toward Efficient Learning for Complex Heterogeneous Graphs

TMLR Paper6912 Authors

Abstract: Heterogeneous graphs—comprising diverse node and edge types connected through varied relations—are ubiquitous in real-world applications. Message-passing heterogeneous graph neural networks (HGNNs) have emerged as a powerful model class for such data. However, existing HGNNs typically allocate a separate set of learnable weights for each relation type to model relational heterogeneity. Despite their promise, these models are effective primarily on simple heterogeneous graphs with only a few relation types. In this paper, we show that this standard design inherently leads to parameter explosion (the number of learnable parameters grows rapidly with the number of relation types) and relation collapse (the model loses the ability to distinguish among different relations). These issues make existing HGNNs inefficient or impractical for complex heterogeneous graphs with many relation types. To address these challenges, we propose Blend&Grind-HGNN (BG-HGNN), a unified feature-representation framework that integrates and distills relational heterogeneity into a shared low-dimensional feature space. This design eliminates the need for relation-specific parameter sets and enables efficient, expressive learning even as the number of relations grows. Empirically, BG-HGNN achieves substantial gains over state-of-the-art HGNNs—improving parameter efficiency by up to 28.96 $\times$ and training throughput by up to up to 110.30 $\times$—while matching or surpassing their accuracy on complex heterogeneous graphs.

Submission Type: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Piyush_Rai1

Submission Number: 6912