@inproceedings{fu-etal-2025-mark,
title = "{MARK}: Multi-agent Collaboration with Ranking Guidance for Text-attributed Graph Clustering",
author = "Fu, Yiwei and
Zhang, Yuxing and
Chen, Chunchun and
Ma, Jianwen and
Yuan, Quan and
Tu, Rong-Cheng and
Huang, Xinli and
Ye, Wei and
Luo, Xiao and
Deng, Minghua",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.314/",
doi = "10.18653/v1/2025.findings-acl.314",
pages = "6057--6072",
ISBN = "979-8-89176-256-5",
abstract = "This paper studies the problem of text-attributed graph clustering, which aims to cluster each node into different groups using both textual attributes and structural information. Although graph neural networks (GNNs) have been proposed to solve this problem, their performance is usually limited when uncertain nodes are near the cluster boundaries due to label scarcity. In this paper, we introduce a new perspective of leveraging large language models (LLMs) to enhance text-attributed graph clustering and develop a novel approach named Multi-agent Collaboration with Ranking Guidance (MARK). The core of our MARK is to generate reliable guidance using the collaboration of three LLM-based agents as ranking-based supervision signals. In particular, we first conduct the coarse graph clustering, and utilize a concept agent to induce the semantics of each cluster. Then, we infer the robustness under perturbations to identify uncertain nodes and use a generation agent to produce synthetic text that closely aligns with their topology. An inference agent is adopted to provide the ranking semantics for each uncertain node in comparison to its synthetic counterpart. The consistent feedback between uncertain and synthetic texts is identified as reliable guidance for fine-tuning the clustering model within a ranking-based supervision objective. Experimental results on various benchmark datasets validate the effectiveness of the proposed MARK compared with competing baselines."
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<abstract>This paper studies the problem of text-attributed graph clustering, which aims to cluster each node into different groups using both textual attributes and structural information. Although graph neural networks (GNNs) have been proposed to solve this problem, their performance is usually limited when uncertain nodes are near the cluster boundaries due to label scarcity. In this paper, we introduce a new perspective of leveraging large language models (LLMs) to enhance text-attributed graph clustering and develop a novel approach named Multi-agent Collaboration with Ranking Guidance (MARK). The core of our MARK is to generate reliable guidance using the collaboration of three LLM-based agents as ranking-based supervision signals. In particular, we first conduct the coarse graph clustering, and utilize a concept agent to induce the semantics of each cluster. Then, we infer the robustness under perturbations to identify uncertain nodes and use a generation agent to produce synthetic text that closely aligns with their topology. An inference agent is adopted to provide the ranking semantics for each uncertain node in comparison to its synthetic counterpart. The consistent feedback between uncertain and synthetic texts is identified as reliable guidance for fine-tuning the clustering model within a ranking-based supervision objective. Experimental results on various benchmark datasets validate the effectiveness of the proposed MARK compared with competing baselines.</abstract>
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%0 Conference Proceedings
%T MARK: Multi-agent Collaboration with Ranking Guidance for Text-attributed Graph Clustering
%A Fu, Yiwei
%A Zhang, Yuxing
%A Chen, Chunchun
%A Ma, Jianwen
%A Yuan, Quan
%A Tu, Rong-Cheng
%A Huang, Xinli
%A Ye, Wei
%A Luo, Xiao
%A Deng, Minghua
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F fu-etal-2025-mark
%X This paper studies the problem of text-attributed graph clustering, which aims to cluster each node into different groups using both textual attributes and structural information. Although graph neural networks (GNNs) have been proposed to solve this problem, their performance is usually limited when uncertain nodes are near the cluster boundaries due to label scarcity. In this paper, we introduce a new perspective of leveraging large language models (LLMs) to enhance text-attributed graph clustering and develop a novel approach named Multi-agent Collaboration with Ranking Guidance (MARK). The core of our MARK is to generate reliable guidance using the collaboration of three LLM-based agents as ranking-based supervision signals. In particular, we first conduct the coarse graph clustering, and utilize a concept agent to induce the semantics of each cluster. Then, we infer the robustness under perturbations to identify uncertain nodes and use a generation agent to produce synthetic text that closely aligns with their topology. An inference agent is adopted to provide the ranking semantics for each uncertain node in comparison to its synthetic counterpart. The consistent feedback between uncertain and synthetic texts is identified as reliable guidance for fine-tuning the clustering model within a ranking-based supervision objective. Experimental results on various benchmark datasets validate the effectiveness of the proposed MARK compared with competing baselines.
%R 10.18653/v1/2025.findings-acl.314
%U https://aclanthology.org/2025.findings-acl.314/
%U https://doi.org/10.18653/v1/2025.findings-acl.314
%P 6057-6072
Markdown (Informal)
[MARK: Multi-agent Collaboration with Ranking Guidance for Text-attributed Graph Clustering](https://aclanthology.org/2025.findings-acl.314/) (Fu et al., Findings 2025)
ACL
- Yiwei Fu, Yuxing Zhang, Chunchun Chen, Jianwen Ma, Quan Yuan, Rong-Cheng Tu, Xinli Huang, Wei Ye, Xiao Luo, and Minghua Deng. 2025. MARK: Multi-agent Collaboration with Ranking Guidance for Text-attributed Graph Clustering. In Findings of the Association for Computational Linguistics: ACL 2025, pages 6057–6072, Vienna, Austria. Association for Computational Linguistics.