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

2024-12-11

arXiv.org

AI Summary

Existing heterogeneous graph neural networks (HGNNs) rely on predefined schemas and manual preprocessing for graphs lacking prior type information and exhibiting non-uniform feature formats, while large language model (LLM)-based approaches often neglect heterogeneity. Method: We propose LLM-GNN, a novel collaborative framework that enables end-to-end automatic format understanding, dynamic type induction, and cross-source feature alignment: an LLM performs semantic parsing of node/edge types to generate a structured schema; an adaptive module aligns heterogeneous features; and a lightweight GNN learns structured representations. Contribution/Results: Our method requires no type annotations or manual preprocessing. We provide theoretical guarantees on representation consistency and convergence. Evaluated on five standard heterogeneous graph benchmarks, LLM-GNN achieves an average 12.7% improvement in downstream task performance over state-of-the-art baselines.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• Handling heterogeneous graphs with diverse node and edge types
• Integrating Large Language Models for graph representation learning
• Eliminating need for type information and preprocessing in graph learning

Innovation

• LLM for data format summarization
• GNN for targeted learning
• No type info preprocessing