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Bootstrapping Heterogeneous Graph Representation Learning via Large Language Models: A Generalized Approach

Hang Gao, Chenhao Zhang, Fengge Wu, Junsuo Zhao, Changwen Zheng, Huaping Liu

TL;DR

This work tackles learning on heterogeneous graphs when node and edge types are unknown. It proposes GHGRL, a three-module framework that combines LLMs for automatic type generation and feature alignment with a dedicated Parameter Adaptive GNN (PAGNN) that performs type-aware message passing. Theoretical analysis shows GHGRL can prevent over-smoothing across different node types, and experiments on standard and newly created heterogeneous datasets demonstrate strong performance without requiring type labels. The approach broadens the applicability of graph representation learning to irregular and diverse graph data, with publicly available code and datasets for reproducibility.

Abstract

Graph representation learning methods are highly effective in handling complex non-Euclidean data by capturing intricate relationships and features within graph structures. However, traditional methods face challenges when dealing with heterogeneous graphs that contain various types of nodes and edges due to the diverse sources and complex nature of the data. Existing Heterogeneous Graph Neural Networks (HGNNs) have shown promising results but require prior knowledge of node and edge types and unified node feature formats, which limits their applicability. Recent advancements in graph representation learning using Large Language Models (LLMs) offer new solutions by integrating LLMs' data processing capabilities, enabling the alignment of various graph representations. Nevertheless, these methods often overlook heterogeneous graph data and require extensive preprocessing. To address these limitations, we propose a novel method that leverages the strengths of both LLM and GNN, allowing for the processing of graph data with any format and type of nodes and edges without the need for type information or special preprocessing. Our method employs LLM to automatically summarize and classify different data formats and types, aligns node features, and uses a specialized GNN for targeted learning, thus obtaining effective graph representations for downstream tasks. Theoretical analysis and experimental validation have demonstrated the effectiveness of our method.

Bootstrapping Heterogeneous Graph Representation Learning via Large Language Models: A Generalized Approach

TL;DR

This work tackles learning on heterogeneous graphs when node and edge types are unknown. It proposes GHGRL, a three-module framework that combines LLMs for automatic type generation and feature alignment with a dedicated Parameter Adaptive GNN (PAGNN) that performs type-aware message passing. Theoretical analysis shows GHGRL can prevent over-smoothing across different node types, and experiments on standard and newly created heterogeneous datasets demonstrate strong performance without requiring type labels. The approach broadens the applicability of graph representation learning to irregular and diverse graph data, with publicly available code and datasets for reproducibility.

Abstract

Graph representation learning methods are highly effective in handling complex non-Euclidean data by capturing intricate relationships and features within graph structures. However, traditional methods face challenges when dealing with heterogeneous graphs that contain various types of nodes and edges due to the diverse sources and complex nature of the data. Existing Heterogeneous Graph Neural Networks (HGNNs) have shown promising results but require prior knowledge of node and edge types and unified node feature formats, which limits their applicability. Recent advancements in graph representation learning using Large Language Models (LLMs) offer new solutions by integrating LLMs' data processing capabilities, enabling the alignment of various graph representations. Nevertheless, these methods often overlook heterogeneous graph data and require extensive preprocessing. To address these limitations, we propose a novel method that leverages the strengths of both LLM and GNN, allowing for the processing of graph data with any format and type of nodes and edges without the need for type information or special preprocessing. Our method employs LLM to automatically summarize and classify different data formats and types, aligns node features, and uses a specialized GNN for targeted learning, thus obtaining effective graph representations for downstream tasks. Theoretical analysis and experimental validation have demonstrated the effectiveness of our method.

Paper Structure

This paper contains 41 sections, 2 theorems, 27 equations, 6 figures, 11 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Heterogeneous Graph Representation Learning.
  4. LLMs for Graphs.
  5. Methodology
  6. Type Generation
  7. LLM Processing
  8. Learning with GNN
  9. Format alignment block.
  10. Content processing block.
  11. Regular learning block.
  12. Analysis
  13. Experiments
  14. Comparison with State of the Art Methods
  15. Baselines.
  16. ...and 26 more sections

Key Result

Theorem 1

Given a connected graph $G = \{\mathcal{V}, \mathcal{E}\}$ with node features $\{\bm{x}_{i}\}_{i=1}^{|\mathcal{V}|}$ and LLM $f(\cdot)$, $\widetilde{g}(\cdot)$ can avoid the over-smoothing described in Equation eq:os for the node features, i.e., we have: where for node $i$ and $j$ that satisfying $\phi(i) \neq \phi(j)$, $\Tilde{\bm{h}}_{i}$ and $\Tilde{\bm{h}}_{j}$ are linearly independent.

Figures (6)

  • Figure 1: Demonstration of different methods.
  • Figure 2: The framework of the proposed method. The snowflake symbol represents the fixed model parameters, while the flame represents the model parameters involved in training.
  • Figure 3: Data representations at different stages of the model after dimensionality reduction using the t-SNE method. Different colors represent distinct types of nodes.
  • Figure 4: Data representations at different stages of the model after dimensionality reduction using the t-SNE method. Different colors represent distinct classes of nodes.
  • Figure 5: Demonstration of different methods.
  • ...and 1 more figures

Theorems & Definitions (4)

  • Theorem 1
  • Corollary 2
  • proof
  • proof