2nd Workshop on Frontiers in Graph Machine Learning for the Large Model Era

Qingyun Sun; Ziwei Zhang; Xingcheng Fu; Lingjuan Lyu; Yangqiu Song; Jianxin Li; Philip S. Yu

doi:10.1145/3770855.3818239

2nd Workshop on Frontiers in Graph Machine Learning for the Large Model Era

Qingyun Sun^*
, Ziwei Zhang^*
, Xingcheng Fu
, Lingjuan Lyu
, Yangqiu Song
, Jianxin Li
, Philip S. Yu

^*Corresponding author for this work

School of Computer Science and Engineering

Guangxi Normal University
Sony AI
Hong Kong University of Science and Technology
University of Illinois at Chicago

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The "2nd Frontiers in Graph Machine Learning for the Large Model Era (GMLLM'26)"workshop focuses on advancing graph machine learning (GML) techniques in the context of large-scale foundation models. Graphs offer a principled way to represent structured and relational data, making them essential for capturing complex dependencies in knowledge, systems, and behaviors. As the scale and influence of foundation models grow, graph learning is well positioned to enhance model robustness, improve interpretability, and integrate domain-specific relational priors. This workshop explores how graph learning can support emerging challenges in knowledge reasoning, temporal and multi-hop inference, and AI systems. It also investigates how advances in representation learning, structure-aware generalization, and efficient graph processing can contribute to trustworthy and scalable AI systems. By convening experts in graph learning, knowledge management, and LLMs, the workshop aims to identify core challenges and opportunities of GML in the large model era.

Original language	English
Title of host publication	KDD 2026 - Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2
Publisher	Association for Computing Machinery
Pages	13437-13438
Number of pages	2
ISBN (Electronic)	9798400722592
DOIs	https://doi.org/10.1145/3770855.3818239
State	Published - 8 Aug 2026
Event	32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1, KDD 2026 - Jeju Island, Korea, Republic of Duration: 9 Aug 2026 → 13 Aug 2026

Publication series

Name	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Volume	2-B
ISSN (Print)	2154-817X

Conference

Conference	32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1, KDD 2026
Country/Territory	Korea, Republic of
City	Jeju Island
Period	9/08/26 → 13/08/26

Keywords

graph foundation models
graph machine learning
graph neural networks
knowledge graph reasoning
large language models

Access to Document

10.1145/3770855.3818239

Cite this

Sun, Q., Zhang, Z., Fu, X., Lyu, L., Song, Y., Li, J., & Yu, P. S. (2026). 2nd Workshop on Frontiers in Graph Machine Learning for the Large Model Era. In KDD 2026 - Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (pp. 13437-13438). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. 2-B). Association for Computing Machinery . https://doi.org/10.1145/3770855.3818239

Sun, Qingyun ; Zhang, Ziwei ; Fu, Xingcheng et al. / 2nd Workshop on Frontiers in Graph Machine Learning for the Large Model Era. KDD 2026 - Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2. Association for Computing Machinery , 2026. pp. 13437-13438 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

@inproceedings{97789de522f348a8b156472cf2170344,

title = "2nd Workshop on Frontiers in Graph Machine Learning for the Large Model Era",

abstract = "The {"}2nd Frontiers in Graph Machine Learning for the Large Model Era (GMLLM'26){"}workshop focuses on advancing graph machine learning (GML) techniques in the context of large-scale foundation models. Graphs offer a principled way to represent structured and relational data, making them essential for capturing complex dependencies in knowledge, systems, and behaviors. As the scale and influence of foundation models grow, graph learning is well positioned to enhance model robustness, improve interpretability, and integrate domain-specific relational priors. This workshop explores how graph learning can support emerging challenges in knowledge reasoning, temporal and multi-hop inference, and AI systems. It also investigates how advances in representation learning, structure-aware generalization, and efficient graph processing can contribute to trustworthy and scalable AI systems. By convening experts in graph learning, knowledge management, and LLMs, the workshop aims to identify core challenges and opportunities of GML in the large model era.",

keywords = "graph foundation models, graph machine learning, graph neural networks, knowledge graph reasoning, large language models",

author = "Qingyun Sun and Ziwei Zhang and Xingcheng Fu and Lingjuan Lyu and Yangqiu Song and Jianxin Li and Yu, \{Philip S.\}",

note = "Publisher Copyright: {\textcopyright} 2026 Owner/Author.; 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1, KDD 2026 ; Conference date: 09-08-2026 Through 13-08-2026",

year = "2026",

month = aug,

day = "8",

doi = "10.1145/3770855.3818239",

language = "英语",

series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",

publisher = "Association for Computing Machinery ",

pages = "13437--13438",

booktitle = "KDD 2026 - Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2",

address = "美国",

}

Sun, Q , Zhang, Z, Fu, X, Lyu, L, Song, Y, Li, J & Yu, PS 2026, 2nd Workshop on Frontiers in Graph Machine Learning for the Large Model Era. in KDD 2026 - Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. 2-B, Association for Computing Machinery , pp. 13437-13438, 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1, KDD 2026, Jeju Island, Korea, Republic of, 9/08/26. https://doi.org/10.1145/3770855.3818239

2nd Workshop on Frontiers in Graph Machine Learning for the Large Model Era. / Sun, Qingyun ; Zhang, Ziwei; Fu, Xingcheng et al.
KDD 2026 - Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2. Association for Computing Machinery , 2026. p. 13437-13438 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. 2-B).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - 2nd Workshop on Frontiers in Graph Machine Learning for the Large Model Era

AU - Sun, Qingyun

AU - Zhang, Ziwei

AU - Fu, Xingcheng

AU - Lyu, Lingjuan

AU - Song, Yangqiu

AU - Li, Jianxin

AU - Yu, Philip S.

PY - 2026/8/8

Y1 - 2026/8/8

N2 - The "2nd Frontiers in Graph Machine Learning for the Large Model Era (GMLLM'26)"workshop focuses on advancing graph machine learning (GML) techniques in the context of large-scale foundation models. Graphs offer a principled way to represent structured and relational data, making them essential for capturing complex dependencies in knowledge, systems, and behaviors. As the scale and influence of foundation models grow, graph learning is well positioned to enhance model robustness, improve interpretability, and integrate domain-specific relational priors. This workshop explores how graph learning can support emerging challenges in knowledge reasoning, temporal and multi-hop inference, and AI systems. It also investigates how advances in representation learning, structure-aware generalization, and efficient graph processing can contribute to trustworthy and scalable AI systems. By convening experts in graph learning, knowledge management, and LLMs, the workshop aims to identify core challenges and opportunities of GML in the large model era.

AB - The "2nd Frontiers in Graph Machine Learning for the Large Model Era (GMLLM'26)"workshop focuses on advancing graph machine learning (GML) techniques in the context of large-scale foundation models. Graphs offer a principled way to represent structured and relational data, making them essential for capturing complex dependencies in knowledge, systems, and behaviors. As the scale and influence of foundation models grow, graph learning is well positioned to enhance model robustness, improve interpretability, and integrate domain-specific relational priors. This workshop explores how graph learning can support emerging challenges in knowledge reasoning, temporal and multi-hop inference, and AI systems. It also investigates how advances in representation learning, structure-aware generalization, and efficient graph processing can contribute to trustworthy and scalable AI systems. By convening experts in graph learning, knowledge management, and LLMs, the workshop aims to identify core challenges and opportunities of GML in the large model era.

KW - graph foundation models

KW - graph machine learning

KW - graph neural networks

KW - knowledge graph reasoning

KW - large language models

UR - https://www.scopus.com/pages/publications/105051051908

U2 - 10.1145/3770855.3818239

DO - 10.1145/3770855.3818239

M3 - 会议稿件

AN - SCOPUS:105051051908

T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

SP - 13437

EP - 13438

BT - KDD 2026 - Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2

PB - Association for Computing Machinery

T2 - 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1, KDD 2026

Y2 - 9 August 2026 through 13 August 2026

ER -

Sun Q , Zhang Z, Fu X, Lyu L, Song Y, Li J et al. 2nd Workshop on Frontiers in Graph Machine Learning for the Large Model Era. In KDD 2026 - Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2. Association for Computing Machinery . 2026. p. 13437-13438. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). doi: 10.1145/3770855.3818239

2nd Workshop on Frontiers in Graph Machine Learning for the Large Model Era

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Keywords

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