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A Survey of Quantized Graph Representation Learning: Connecting Graph Structures with Large Language Models

Qika Lin, Zhen Peng, Kaize Shi, Kai He, Yiming Xu, Jian Zhang, Erik Cambria, Mengling Feng

TL;DR

This survey examines quantized graph representation learning (QGR) as a remedy to inefficiencies and opacity in traditional continuous graph embeddings, highlighting its capacity to integrate with large language models through discrete codes. It catalogs quantization methods (PQ, VQ, FSQ, ASA), surveys encoder–decoder–quantizer architectures, and reviews strategies, designs, KG-focused quantization, and diverse applications. The discussion covers code-dependence learning and the integration of QGR with LLMs, including practical case studies and cross-domain use cases in graphs, knowledge graphs, and molecular tasks. The authors outline future directions toward interpretable codebooks, advanced quantization techniques, a unified graph foundation model, and graph retrieval-augmented generation frameworks that leverage QGR.

Abstract

Recent years have witnessed rapid advances in graph representation learning, with the continuous embedding approach emerging as the dominant paradigm. However, such methods encounter issues regarding parameter efficiency, interpretability, and robustness. Thus, Quantized Graph Representation (QGR) learning has recently gained increasing interest, which represents the graph structure with discrete codes instead of conventional continuous embeddings. Given its analogous representation form to natural language, QGR also possesses the capability to seamlessly integrate graph structures with large language models (LLMs). As this emerging paradigm is still in its infancy yet holds significant promise, we undertake this thorough survey to promote its rapid future prosperity. We first present the background of the general quantization methods and their merits. Moreover, we provide an in-depth demonstration of current QGR studies from the perspectives of quantized strategies, training objectives, distinctive designs, knowledge graph quantization, and applications. We further explore the strategies for code dependence learning and integration with LLMs. At last, we give discussions and conclude future directions, aiming to provide a comprehensive picture of QGR and inspire future research.

A Survey of Quantized Graph Representation Learning: Connecting Graph Structures with Large Language Models

TL;DR

This survey examines quantized graph representation learning (QGR) as a remedy to inefficiencies and opacity in traditional continuous graph embeddings, highlighting its capacity to integrate with large language models through discrete codes. It catalogs quantization methods (PQ, VQ, FSQ, ASA), surveys encoder–decoder–quantizer architectures, and reviews strategies, designs, KG-focused quantization, and diverse applications. The discussion covers code-dependence learning and the integration of QGR with LLMs, including practical case studies and cross-domain use cases in graphs, knowledge graphs, and molecular tasks. The authors outline future directions toward interpretable codebooks, advanced quantization techniques, a unified graph foundation model, and graph retrieval-augmented generation frameworks that leverage QGR.

Abstract

Recent years have witnessed rapid advances in graph representation learning, with the continuous embedding approach emerging as the dominant paradigm. However, such methods encounter issues regarding parameter efficiency, interpretability, and robustness. Thus, Quantized Graph Representation (QGR) learning has recently gained increasing interest, which represents the graph structure with discrete codes instead of conventional continuous embeddings. Given its analogous representation form to natural language, QGR also possesses the capability to seamlessly integrate graph structures with large language models (LLMs). As this emerging paradigm is still in its infancy yet holds significant promise, we undertake this thorough survey to promote its rapid future prosperity. We first present the background of the general quantization methods and their merits. Moreover, we provide an in-depth demonstration of current QGR studies from the perspectives of quantized strategies, training objectives, distinctive designs, knowledge graph quantization, and applications. We further explore the strategies for code dependence learning and integration with LLMs. At last, we give discussions and conclude future directions, aiming to provide a comprehensive picture of QGR and inspire future research.

Paper Structure

This paper contains 17 sections, 18 equations, 3 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. General Quantization Methods
  3. Quantized Graph Learning Framework
  4. Quantized Strategies
  5. Training Objectives
  6. Distinctive Designs
  7. KG Quantization
  8. Application Scenarios
  9. Code Dependence Learning
  10. Integration with LLMs
  11. Future Directions
  12. Merits of Quantized Graph Representation
  13. Algorithm for RVQ
  14. General Framework of QGR Studies
  15. Comparison between SSQR and ReaLM
  16. ...and 2 more sections

Figures (3)

  • Figure 1: Illustration of different strategies for graph representations. (a) is the continuous graph representation. (b) is the quantized graph representation which represents the graph structure with discrete codes instead of conventional continuous embeddings.
  • Figure 2: The general framework of the QGR studies, which mainly comprises an encoder, decoder, and quantization process. Training objectives of different levels can be utilized. By combining a predictor, multiple applications can be realized.
  • Figure 3: Illustration of integrating QGR with LLMs.