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BG-HGNN: Toward Efficient Learning for Complex Heterogeneous Graphs

Junwei Su, Lingjun Mao, Zheng Da, Chuan Wu

TL;DR

<3-5 sentence high-level summary>BG-HGNN tackles parameter explosion and relation collapse in heterogeneous graph neural networks by embedding diverse relation types into a unified low-dimensional space using type-aware random encodings and a learnable low-rank fusion of node attributes, node-type signals, and relation-type context. The framework then applies shared-parameter homogeneous GNN layers, achieving substantial parameter and throughput gains while maintaining or improving predictive accuracy, especially on graphs with many relation types. Theoretical analysis shows reduced parameter complexity and strictly enhanced expressiveness compared to canonical HGNNs, and extensive experiments on 11 benchmarks confirm practical scalability and effectiveness. These results suggest BG-HGNN as a scalable, expressive alternative for complex heterogeneous graphs in real-world applications.

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 28.96x and training throughput by up to 110.30x, while matching or surpassing their accuracy on complex heterogeneous graphs.

BG-HGNN: Toward Efficient Learning for Complex Heterogeneous Graphs

TL;DR

<3-5 sentence high-level summary>BG-HGNN tackles parameter explosion and relation collapse in heterogeneous graph neural networks by embedding diverse relation types into a unified low-dimensional space using type-aware random encodings and a learnable low-rank fusion of node attributes, node-type signals, and relation-type context. The framework then applies shared-parameter homogeneous GNN layers, achieving substantial parameter and throughput gains while maintaining or improving predictive accuracy, especially on graphs with many relation types. Theoretical analysis shows reduced parameter complexity and strictly enhanced expressiveness compared to canonical HGNNs, and extensive experiments on 11 benchmarks confirm practical scalability and effectiveness. These results suggest BG-HGNN as a scalable, expressive alternative for complex heterogeneous graphs in real-world applications.

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 28.96x and training throughput by up to 110.30x, while matching or surpassing their accuracy on complex heterogeneous graphs.
Paper Structure (53 sections, 3 theorems, 48 equations, 10 figures, 4 tables, 3 algorithms)

This paper contains 53 sections, 3 theorems, 48 equations, 10 figures, 4 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. HGNNs Categories.
  3. Limitation of Existing HGNNs.
  4. Preliminary and Related Works
  5. Heterogeneous Graphs.
  6. Heterogeneous Graph Neural Networks.
  7. Methodology
  8. Blend&Grind-HGNN(BG-HGNN)
  9. Overview.
  10. Unified Feature Space Construction
  11. Type-aware Random Encoding.
  12. Node-type Encoding.
  13. Edge-type Encoding.
  14. Low-rank Interaction Fusion.
  15. Low-rank approximation.
  16. ...and 38 more sections

Key Result

Proposition 3.1

Consider a heterogeneous graph with $|\mathcal{R}| > 0$ relation types. Then the parameter complexity of the canonical HGNN described above is i.e., linear in both the number of relations and the number of layers. In contrast, for an BG-HGNN model with $L$ layers, the parameter complexity of its low-rank fusion module and shared-parameter message-passing layers is where $r$ is the rank used in t

Figures (10)

  • Figure 1: Figure \ref{['fig:hg']} provides an illustrative example of a heterogeneous graph in the context of a citation network. Nodes of different colors represent distinct node types (e.g., authors, papers, institutions), each associated with unique feature vectors varying in both attributes and dimensions. Figure \ref{['fig:hgnn']} illustrates the structure of a single-layer heterogeneous graph neural network (HGNN). In this architecture, independent parameter spaces are employed to project heterogeneous node features into a unified latent representation. The projected embeddings are subsequently aggregated and processed through a nonlinear function $\sigma(.)$, yielding a final representation for each target node.
  • Figure 2: Overview of the BG-HGNN framework.
  • Figure 3: Parameter efficiency (left) and performance gain (right) of BG-HGNN under varying relation complexity. Parameter gain reflects the parameter reduction ratio compared to RGCN (i.e., RGCN parameters divided by ours), while performance gain measures the relative accuracy improvement. Relation Count is the number of distinct relations, and Relation Density is the ratio of relations to edges in the dataset.
  • Figure 4: Comparison across encoding/fusion methods and modeling strategies. The first two figures show accuracy and convergence efficiency, where C and K denote Concat and Kronecker fusion, and O and R represent One-hot and Random relation encoding. Each combination reflects a distinct design choice. The latter two show the impact of rank selection and feature combinations on BG-HGNN. In figure (d), F, N, and R denote input node features, node type, and relation type, and each method corresponds to a different combination of these features.
  • Figure 5: The plots depict the inferred attention/activation from our method and the ground truth for meta-path discovery featuring four node types: Author, Paper, Term, and Subject. Both matrices are of size $50\times50$, showing the interactions among these node types.
  • ...and 5 more figures

Theorems & Definitions (7)

  • Proposition 3.1
  • Definition 1: Relative Expressiveness of HGNNs
  • Proposition 3.2: Relative Expressiveness
  • proof
  • Lemma A.1: Relation Collapse
  • proof
  • proof : Proof of Proposition \ref{['prop:expressive']}