@inproceedings{lin-etal-2026-survey-quantized,
title = "A Survey of Quantized Graph Representation Learning: Connecting Graph Structures with Large Language Models",
author = "Lin, Qika and
Peng, Zhen and
Shi, Kaize and
He, Kai and
Xu, Yiming and
Zhang, Jian and
Cambria, Erik and
Feng, Mengling",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.135/",
pages = "2765--2778",
ISBN = "979-8-89176-462-0",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T A Survey of Quantized Graph Representation Learning: Connecting Graph Structures with Large Language Models
%A Lin, Qika
%A Peng, Zhen
%A Shi, Kaize
%A He, Kai
%A Xu, Yiming
%A Zhang, Jian
%A Cambria, Erik
%A Feng, Mengling
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F lin-etal-2026-survey-quantized
%X 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.
%U https://aclanthology.org/2026.findings-emnlp.135/
%P 2765-2778
Markdown (Informal)
[A Survey of Quantized Graph Representation Learning: Connecting Graph Structures with Large Language Models](https://aclanthology.org/2026.findings-emnlp.135/) (Lin et al., Findings 2026)
ACL
- Qika Lin, Zhen Peng, Kaize Shi, Kai He, Yiming Xu, Jian Zhang, Erik Cambria, and Mengling Feng. 2026. A Survey of Quantized Graph Representation Learning: Connecting Graph Structures with Large Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 2765–2778, Budapest, Hungary. Association for Computational Linguistics.