A Survey of Quantized Graph Representation Learning: Connecting Graph Structures with Large Language Models
It provides a comprehensive overview for researchers in graph learning and AI, but is incremental as it surveys existing work rather than presenting new findings.
This survey addresses the problem of inefficiencies in continuous graph embeddings by reviewing quantized graph representation learning, which uses discrete codes to improve parameter efficiency and enable integration with large language models, though no concrete results or numbers are provided as it is a review paper.
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.