MARK: Multi-agent Collaboration with Ranking Guidance for Text-attributed Graph Clustering
Authors: yiwei Fu, Yuxing Zhang, Chunchun Chen, JianwenMa, Quan Yuan, Rong-Cheng Tu, Xinli Huang, Wei Ye, Xiao Luo, Minghua Deng
Accepted at: acl25

MARK: Multi-agent Collaboration with Ranking Guidance for Text-attributed Graph Clustering

Yiwei Fu1*, Yuxing Zhang2*, Chunchun Chen3*, Jianwen Ma1, Quan Yuan2, Rong-Cheng Tu4, Xinli Huang2, Wei Ye3,5, Xiao Luo6†, Minghua Deng1†
1 Peking University  2 East China Normal University  3 Tongji University  4 Nanyang Technological University  5 Shanghai Innovation Institute  6 UCLA
{fuyw, jianwen_ma}@stu.pku.edu.cn, {zhangyuxing, quanyuan}@stu.ecnu.edu.cn
{c2chen, yew}@tongji.edu.cn, rongcheng.tu@ntu.edu.sg
xlhuang@cs.ecnu.edu.cn, xiaoluo@cs.ucla.edu, dengmh@math.pku.edu.cn

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.

1 Introduction
Graph Neural Networks (GNNs) (Hamilton et al., 2017; Veličković et al., 2017), a popular method to handle graph, can learn the overall structural information within graphs but are unable to directly process the informative text associated with nodes. With the advent of the LLMs era (Guo et al., 2025), research on integrating LLMs with GNNs for TAGs has garnered increasing attention in recent years (Jin et al., 2024). However, most studies (Chen et al., 2024c; He et al., 2024; Huang et al., 2024; Bi et al., 2024) have focused on supervised tasks, such as node classification and link prediction, leaving text-attributed graph clustering—an unsupervised task—largely underexplored.

Text-attributed graph clustering utilizes both structural and textual information to assign nodes to distinct clusters, ensuring that similar nodes are grouped together while dissimilar nodes are dispersed across separate clusters. Since cluster assignments can intuitively reflect communities within a graph, many real-world problems can be formulated as graph clustering tasks, such as identifying friend groups in social networks (Hartup, 2022) or recommending papers to researchers within the same field (Wu et al., 2022).

Existing methods (Trivedi et al., 2024), after obtaining cluster assignments from the clustering model, utilize an LLM-based agent to decompose the task of querying the clustering categories of nodes into several sub-tasks, which are then processed sequentially to derive the final feedback. After filtering out potential incorrect feedback based on the LLM’s prediction confidence scores, the clustering model is then fine-tuned using cross-entropy loss with the feedback-based pseudo-labels. However, three challenges still remain: (1) Can we use multi-agent cooperation to query clustering categories? Queries based on a single agent lack breadth of knowledge, resulting in an overly narrow perspective (Guo et al., 2024). (2) Can we develop filtering strategies that consider both text semantics and graph topology? The noisy feedback filtering strategy that relies solely on prediction confidence utilizes the prior knowledge of LLMs but fails to leverage the topological perspective from the graph. (3) Can we choose a more robust fine-tuning loss
Figure 1: Potential challenges exist before and advantages of MARK compared with traditional methods.

function? When the pseudo-labels contain noisy feedbacks that may be missed during filtering, the cross-entropy loss is not robust, leading to overfitting on incorrect clusters (Feng et al., 2021).

In this paper, we introduce a new perspective of leveraging 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. Specifically, through empirical analysis, we find that fine-tuning the graph clustering model on uncertain nodes yields better gains compared to doing so on certain or random nodes. Inspired by this, we perform a graph clustering model on two perturbation graphs in the Graph Clustering Engine. We identify the nodes with inconsistent cluster assignments between the two views as uncertain nodes and employ three agents to query their cluster categories. In the Concept Agent, we first induce the concept of each cluster using the nodes close to the center of each cluster. We calculate the similarity between uncertain nodes and their neighbors within the corresponding ego-graph to select a few representative neighbors for each uncertain node. To reduce the cost of querying neighbor representatives from the LLM, we use a Generation Agent to summarize the neighbor representatives and generate synthetic text with equivalent semantics. In the Inference Agent, we construct the inference prompt by appending the cluster concepts obtained from the Concept Agent to both the uncertain text and the synthetic text. Since the synthetic text captures both the topological and semantic information of the uncertain node, we use the consistency of the agent’s feedback on the uncertain text and the synthetic text as a filtering criterion. The feedback may still be inconsistent with the true labels of uncertain nodes, even after filtering. Therefore, we adopt a ranking-based supervision objective during fine-tuning to enhance the robustness of node representations, thereby mitigating the adverse effects of noise. Overall, the coarse clustering assignments generated by the graph clustering engine provide topological information to the multi-agent framework. In turn, the collaborative decisions from the agents enhance the performance of the graph clustering engine.

To summarize, the contributions are as follows: (1) New Perspective. We are the first to connect text-attribute graph clustering with a multi-agent framework for reliable semantics. (2) Novel Methodology. Our MARK leverage the collaboration of a concept agent, a generation agent and an inference agent to provide ranking signals, which are utilized to guide the graph clustering. (3) Extensive Experiments. Experimental results on various benchmark datasets validate the effectiveness of the proposed MARK compared with competing baselines.

2 Related Work

Recently, research on the integration of Large Language Models (LLMs) and Graph Neural Networks (GNNs) in text-attribute graphs has garnered increasing attention (Chen et al., 2023; Zhang et al., 2025; Zhao et al., 2025). Researches can be divided into three categories: (1) LLM-as-encoder. (Wen and Fang, 2023; Zhao et al., 2023a; Jin et al., 2023) uses pre-trained language models (PLMs) (Reimers, 2019) to encode texts, achieving a hybrid architecture of PLM and GNN. To obtain expressive representations, (Zhu et al., 2024) utilizes the hidden embeddings of LLMs to construct node embeddings. (2) LLM-as-predictor. (Chen et al., 2024a; Zhao et al., 2023b; Tang et al., 2024) serializes the graph into various sequences, which are then fed into the LLM for prediction. (Yang et al., 2021; Yasunaga et al., 2022) adjust traditional LLM architecture to conduct joint text and graph encoding. (3) LLM-as-reasoner. Under the guidance of carefully designed prompts, (Chen
et al., 2024b; Bi et al., 2024; Wu et al., 2025) enables LLMs to perform inference tasks for node class or link existence.

The above works almost focus on node classification and link prediction, while text-attributed graph clustering remains under-explored. To the best of our knowledge, GCLR (Trivedi et al., 2024) is currently the only study dedicated to text-attributed graph clustering. GCLR employs a contrastive (Liu et al., 2023a) and pooling-based (Tsitsulin et al., 2023; Bianchi et al., 2020; Ying et al., 2018) attributed graph clustering model as its backbone. GCLR adopts an LLM-as-reasoner architecture described above, where the LLM solely offers feedback for the fine-tuning of the GNN, thereby eliminating the costly pretraining and fine-tuning of LLMs. Despite the success, the three challenges discussed in the previous section require further exploration in this paper.

3 Method

3.1 Problem Formulation

\( \mathcal{G} = (\mathbf{A}, \mathcal{D}, \mathbf{X}) \) is a text-attributed graph (TAG) with the adjacency matrix \( \mathbf{A} \in \{0, 1\}^{N \times N} \), the raw texts \( \mathcal{D} = \{\mathcal{D}^1, \mathcal{D}^2, \ldots, \mathcal{D}^N\} \) and PLM-encoded textual features \( \mathbf{X} = \{\mathbf{x}_1, \mathbf{x}_2, \ldots, \mathbf{x}_N\} \in \mathbb{R}^{N \times D} \) (Reimers, 2019). \( N \) denotes the number of nodes. The node embeddings encoded by GNN are represented by \( \mathbf{H} = \{\mathbf{h}_1, \mathbf{h}_2, \ldots, \mathbf{h}_N\} \in \mathbb{R}^{N \times F} \). \( D \) and \( F \) are the dimensions of node features and embeddings, respectively. Graph clustering aims to partition the nodes in the text-attributed graph \( \mathcal{G} \) into \( K \) disjoint clusters \( \mathcal{C} = \{C_1, C_2, \cdots, C_K\} \). The embedding of the cluster centers is represented by \( \{\mu_1, \mu_2, \ldots, \mu_K\} \in \mathbb{R}^{K \times F} \).

3.2 Overview of MARK

Following the two-stage learning style (Zhang et al., 2023; Trivedi et al., 2024), our aim is to leverage the collaboration of multi-agent to provide ranking signals for the graph clustering.

As described in Figure 2, the proposed MARK includes a graph clustering engine and three supporting agents: (1) Graph clustering engines build upon the shoulders of existing advanced graph clustering models, leveraging their sophisticated representation capabilities to perform contrastive learning on two perturbed views. By seeking robustness in cluster assignments across these two views, uncertain nodes are identified. (2) The concept agent \( \mathcal{M}_{con} \) selects high-confidence nodes from each cluster to induce cluster names. The induced concepts will serve as foundational knowledge for the other two agents, enhancing their capability to execute specific tasks. (3) The generation agent \( \mathcal{M}_{gen} \) synthesizes virtual text by aggregating the neighboring texts of each uncertain node, thereby enhancing data diversity while considering the neighborhood topology. (4) The inference agent \( \mathcal{M}_{inf} \) filters out low-confidence uncertain nodes by evaluating the consistency of feedback from LLMs between uncertain texts and synthetic texts. Finally, we adopt a ranking-based supervised objective to utilize the filtered nodes for fine-tuning the graph clustering engine. In the following, we present them step by step.

3.3 Graph Clustering Engine

After obtaining the coarse clustering assignments, it is typically necessary to obtain feedback on informative nodes from the LLM to fine-tune the clustering model. Existing methods (Trivedi et al., 2024) typically select nodes with high entropy as candidate query nodes. However, nodes with low entropy may also have incorrect clustering assignments; thus, selecting only high-entropy nodes rather than all nodes as candidates for querying can introduce bias. To address this challenge, we have developed a robustness-based mechanism for identifying potentially valuable candidate query nodes. We employ a dual-view contrastive learning framework to detect clustering discrepancies under perturbations, treating these non-robust nodes as uncertain nodes for querying. The fundamental principle behind this mechanism is to find nodes with ambiguous category perceptions, which are prone to inconsistent judgments under minor perturbations.

Specifically, we apply perturbations at both feature level and edge level to obtain two augmented views of the graph, and then feed them separately into a shared GNN encoder to get node representations \( \mathbf{H}', \mathbf{H}'' \in \mathbb{R}^{N \times F} \), where \( F \) means the dimension of the node representations. We align the two views by alignment loss \( \mathcal{L}_{ali} \), as shown below:

\[
\mathcal{L}_{ali} = - \frac{1}{N} \sum_{i=1}^{N} \log \frac{\exp(\operatorname{sim}(\mathbf{h}_i', \mathbf{h}_i'')/\tau)}{\sum_{k=1}^{N} \exp(\operatorname{sim}(\mathbf{h}_i', \mathbf{h}_k'')/\tau)},
\]

where \( \mathbf{h}_i' \) and \( \mathbf{h}_i'' \) are the representations of two augmentations with regard to the \( i \)-th node, \( \operatorname{sim}(*) \) indicates the cosine similarity function, \( \tau \) controls the temperature to adjust the distribution. The clustering loss \( \mathcal{L}_{clu} \) serves as a flexible plugin, enabling
Figure 2: Overview of MARK. A graph clustering engine is introduced to identify uncertain nodes. Three agents (the concept agent, generation agent, and inference agent) collaborate to provide ranking signals for these uncertain nodes. Finally, consistency feedback serves as a reliable guide for fine-tuning the clustering engine.

![Overview of MARK showing three agents and a graph clustering engine](page_186_120_1077_340.png)

Figure 3: Performance comparison of multi-agent collaboration using different node sets.

(a) NMI metric   (b) ACC metric

![Bar charts comparing NMI and ACC metrics for different node sets](page_186_563_1077_180.png)

MARK to utilize the clustering capabilities of any state-of-the-art graph clustering model. The pre-training loss for the engine is defined as follows:

\[
\mathcal{L}_{eng} = \mathcal{L}_{ali} + \mathcal{L}_{clu}.
\] (2)

Subsequently, embeddings \( \mathbf{H}' \) and \( \mathbf{H}'' \) are used to derive the clustering assignments \( C' \) and \( C'' \), respectively. We construct GNN-uncertain node set \( \mathcal{S} \) for querying LLMs, as shown below:

\[
\mathcal{S} = \{ i \in \mathcal{G} \mid C'_i \neq C''_i \}.
\] (3)

An empirical analysis has been performed to explain our motivation. As shown in Figure 3, the results show that, given the ground-truth labels, fine-tuning the clustering model using the set of uncertain nodes \( \mathcal{S} \) yields the most significant performance improvement compared to using certain nodes or randomly selected nodes. This highlights the necessity of prioritizing uncertain nodes (Zhang et al., 2025; Chen et al., 2023).

3.4 Multi-agent Collaboration

Concept Agent for Cluster Induction After obtaining cluster assignments \( \hat{C} = \{\hat{C}_1, \hat{C}_2, \cdots, \hat{C}_K\} \) generated by the graph clustering engine, we select the top-n samples that are closest to the center of each cluster to construct set \( \mathcal{D}_{clo} = \{\mathcal{D}^1_{clo}, \mathcal{D}^2_{clo}, \cdots, \mathcal{D}^K_{clo}\} \). Through querying \( \mathcal{M}_{con} \), we acquire the concept \( \mathcal{F}_{con} \) for each cluster along with its corresponding explanation,

\[
\mathcal{F}_{con} = \mathcal{M}_{con}(\mathcal{D}^1_{clo}, \mathcal{D}^2_{clo}, \cdots, \mathcal{D}^K_{clo}).
\] (4)

The acquired cluster concepts \( \mathcal{F}_{con} \) will be leveraged to craft the following two agents with more precisely defined identities, thereby enhancing their capabilities for specific tasks. The detailed agent design can be found in Figure 6.

Generation Agent for Neighbor Summary In order to filter out the LLM responses in \( S \) that do not take topology into account, it is a natural approach to consider the neighborhood information of these nodes as an aid in the decision-making process (Chen et al., 2024b). Unlike GNNs, which excel at efficiently aggregating information from neighboring nodes, LLMs face significant challenges in this regard. To this end, we use \( \mathcal{M}_{gen} \) to aggregate neighbors and generate virtual synthetic text \( \mathcal{F}_{syn} \). Following the similarity-based neighbor selection strategy (Li et al., 2024), we select the top-k most similar samples from the neighbors of uncertain nodes to form neighbor description set \( \mathcal{D}_{nei} \). Subsequently, we input both the concepts \( \mathcal{F}_{con} \) learned by \( \mathcal{M}_{con} \) and \( \mathcal{D}_{nei} \) into \( \mathcal{M}_{gen} \), which then generates new samples \( \mathcal{F}_{syn} \),

\[
\mathcal{F}^i_{syn} = \mathcal{M}_{gen}(\mathcal{D}^i, \mathcal{D}^i_{nei}, \mathcal{F}_{con}).
\] (5)

The virtual texts not only capture the topological relationships among nodes within the graph but also encapsulate the semantic context associated with those nodes. They play a key role in filtering uncertain nodes for the following agent. The detailed agent design is listed in Figure 7.
Inference Agent for Decision Filtration  We construct the inference prompt by appending the cluster concepts obtained from the Concept Agent to both the uncertain node’s text and the synthetic text. By querying \( \mathcal{M}_{inf} \), we get their inferred cluster categories. Since the synthetic text captures both the topological and semantic information of the uncertain node, we use the consistency of the agent’s feedback on the uncertain text and the synthetic text as a filtering criterion to obtain Agents-resolvable node set \( \mathcal{R} \), as shown below:

\[
\mathcal{F}_{inf}^i, \mathcal{F}_{inf}^{i, syn} = \mathcal{M}_{inf}(\mathcal{D}^i, \mathcal{F}_{syn}, \mathcal{F}_{con}),
\] (6)

\[
\mathcal{R} = \{ i \in \mathcal{S} \mid \mathcal{F}_{inf}^i = \mathcal{F}_{inf}^{i, syn} \}.
\] (7)

The detailed agent design is listed in Figure 8. In response to the LLM’s feedback, we directly leverage the knowledge contained in the generated texts to refine the shallow node embeddings \( \mathbf{X} \), rather than relying on explanations provided by the LLM (Qiao et al., 2025). Specifically, we encode the generated texts corresponding to the nodes in \( \mathcal{R} \) using a Pre-trained Language Model (PLM), then update \( \mathbf{X} \) by averaging the sum of these encodings with the original, which can be expressed as:

\[
\mathbf{x}_i' = \frac{\mathbf{x}_i + \mathrm{PLM}(\mathcal{F}_{syn}^i)}{2},
\] (8)

where \( \mathbf{x}_i' \) denotes the updated features guided by the knowledge of LLMs and \( i \in \mathcal{R} \).

3.5 Fine-Tuning with Ranking Guidance

Three agents collaborate to filter nodes from the GNN-uncertain node set \( \mathcal{S} \), ultimately obtaining the Agents-resolvable node set \( \mathcal{R} \). This process is equivalent to conducting a topology-level and semantic-level ranking of the nodes within the GNN-uncertain node set through the efforts of multiple agents. The filtered nodes are those with high rankings in the \( \mathcal{S} \). We should focus on these uncertain nodes that are considered reliable by the multi-agent system (Luo et al., 2025; Liu et al., 2024a), as they can provide ranking-based supervisory signals for fine-tuning.

Existing methods (Trivedi et al., 2024) use cross-entropy to fine-tune the clustering model. However, the feedback may still be inconsistent with the true labels, even after filtering. Due to the lack of robustness of cross-entropy loss to noisy labels (Zhang and Sabuncu, 2018) and potentially inadequate margins (Liu et al., 2016), generalization performance deteriorates. Therefore, we adopt a ranking-based supervision objective during fine-tuning to enhance the robustness of node representations, thereby mitigating the adverse effects of noise.

We bring the uncertain nodes closer to the cluster identified by the agent while repelling them from other clusters. Specifically, we first calculate the similarity between the nodes in \( \mathcal{R} \) and the cluster centers. We then pair each uncertain node \( i \) with its corresponding cluster center as a positive pair \( \mathcal{F}_{inf}^i \), treating the remaining clusters as negative pairs. We apply contrastive learning to the node-cluster pairs, which acts as a calibration loss for the coarse clustering assignment, expressed as follows:

\[
\mathcal{L}_{cal} = - \frac{1}{|\mathcal{R}|} \sum_{i \in \mathcal{R}} \log \left( \frac{\exp \left( \operatorname{sim} \left( \mathbf{h}_i, \mu_{\mathcal{F}_{inf}^i} \right) / t \right)}{\sum_{k=1}^K \exp \left( \operatorname{sim} \left( \mathbf{h}_i, \mu_k \right) / t \right)} \right),
\] (9)

where \( t \) means the temperature, \( \mu \) denotes the cluster center. Subsequently, we use \( \mathcal{L}_{ft} \) to fine-tune the graph clustering engine, that is,

\[
\mathcal{L}_{ft} = \mathcal{L}_{eng} + \mathcal{L}_{cal}.
\] (10)

Algorithm 1: MARK

Input: \( \mathcal{G} = (\mathbf{A}, \mathbf{X}, \mathcal{D}) \), GNN encoder \( \mathcal{F}_{\Theta} \), training epoch \( T \), multi-agent execution interval \( T' \), learning rate \( \beta \), number of selected nodes \( n \).
1 Use the graph clustering engine for pre-training;
2 Initialize Agents-resolvable node set \( \mathcal{R} \);
3 for \( i \gets 1 \) to \( T \) do
4   Augment \( \mathcal{G} \): Obtain \( \mathcal{G}' \) and \( \mathcal{G}'' \);
5   Encode: \( \mathbf{H}' = \mathcal{F}(\mathcal{G}') \) and \( \mathbf{H}'' = \mathcal{F}(\mathcal{G}'') \);
6   if \( i \% T' == 0 \) then
7     Get the clustering assignments \( \mathcal{C}' \) and \( \mathcal{C}'' \) for the augmented graphs by \( \mathbf{H}' \) and \( \mathbf{H}'' \):
8     Construct the GNN-uncertain node set \( \mathcal{S} \);
9     // Concept Agent
10    Select top-n nodes closest to each cluster center as query samples;
11    Induce concept of each cluster by \( \mathcal{M}_{con} \);
12     // Generation Agent
13    Select neighbors of uncertain nodes in \( \mathcal{S} \) according to similarity ranking;
14    Produce synthetic text for uncertain nodes as a summary of neighbors by \( \mathcal{M}_{gen} \);
15     // Inference Agent
16    Query the clusters for synthetic texts and uncertain texts;
17    Update \( \mathcal{R} \) by filtering uncertain nodes with cluster consistency by \( \mathcal{M}_{inf} \);
18    Update \( \mathbf{X} \) of the nodes in \( \mathcal{Q} \) by Eq. 8;
19    Calculate the fine-tuning loss \( \mathcal{L}_{ft} \) in Eq. 10;
20    Update: \( \Theta \gets \Theta - \beta \cdot \nabla \mathcal{L}_{ft} \);
Output: Final cluster assignments.
Overall, we consider the graph clustering model that provides uncertain nodes as the engine for three agents, establishing a progressive chain that facilitates collaboration among three agents. In turn, the collaborative decisions made by these three agents offer reliable guidance for clustering. The process of MARK is shown in Algorithm 1.

4 Experiments

4.1 Settings

Dataset We evaluate MARK on four widely used text-attributed graphs (Chen et al., 2023): Cora, CiteSeer, PubMed, and WikiCS. The detailed statistics information is listed in Table 1.

<table>
  <tr>
    <th>Dataset</th>
    <th>#Node</th>
    <th>#Edge</th>
    <th>#Classes</th>
    <th>#Type</th>
  </tr>
  <tr>
    <td>Cora</td>
    <td>2,708</td>
    <td>5,429</td>
    <td>7</td>
    <td>Citation</td>
  </tr>
  <tr>
    <td>CiteSeer</td>
    <td>3,186</td>
    <td>4,277</td>
    <td>6</td>
    <td>Citation</td>
  </tr>
  <tr>
    <td>PubMed</td>
    <td>19,717</td>
    <td>44,335</td>
    <td>3</td>
    <td>Citation</td>
  </tr>
  <tr>
    <td>WikiCS</td>
    <td>11,701</td>
    <td>215,863</td>
    <td>10</td>
    <td>Wikipedia</td>
  </tr>
</table>

Table 1: Statistics of used datasets.

Models For the graph clustering engine backbone, we utilize the state-of-the-art DMoN (Tsitulin et al., 2023) and MAGI (Liu et al., 2024b) as the baseline models. We select GPT-4o-mini1 as our agent LLM to generate reliable guidance.

Metrics We adopt four metrics to evaluate clustering results: ACC, NMI, ARI, and F1. The results are reported for five runs with different random seeds. Larger values denote better performance.

4.2 Performance Comparison

To more effectively evaluate MARK’s performance on graph clustering tasks, we adopt two classic clustering baselines, DMoN and MAGI, as the backbone clustering engines, deriving two versions of MARK: MARK-DMoN and MARK-MAGI. We then compare their performance using four metrics across four commonly used graph datasets: Cora, CiteSeer, WikiCS, and PubMed. Table 2 presents the clustering performance of MARK with the two distinct backbones. Additionally, DMoN and MAGI represent the original clustering results, while "PRE-TRAIN" indicates the clustering results after the graph clustering engine. Notably, MARK-DMoN and MARK-MAGI display the final clustering performance enhanced by our multi-agent framework.

As shown in Table 2, MARK-MAGI demonstrates a much more substantial improvement over the MAGI backbone across the Cora, CiteSeer, and WikiCS datasets, while displays a smaller increase observed on PubMed, with 1.80% improvement in NMI. Furthermore, MARK-DMoN exhibits the most significant improvement on the WikiCS dataset, with a 20.80% increase in F1 score. In conclusion, MARK not only excels across various evaluation metrics but also proposes a versatile framework for enhancing existing graph clustering methods.

4.3 Ablation Study

To validate the contributions of each proposed agent to MARK, we conduct comprehensive ablation studies on the Cora and CiteSeer datasets. Initially, the concept agent assigns topics based on a selection of typical and high-confidence samples, which significantly aids in cluster exploration. We remove the concept agent and substitute it with the highest-confidence text within each cluster to represent the cluster, resulting in this variant "W/O AGENT 1". To illustrate the necessity of the generation agent, we replace the virtual text synthesized by the generation agent with text randomly sampled from its neighborhood, referred to as "W/O AGENT 2". To demonstrate the effectiveness of selectively accepting labels predicted by the inference agent, we introduce "W/O AGENT 3", in which all labels are directly assigned by the inference agent without any filtering.

As illustrated in Table 3, removing the concept agent results in the most significant performance drop, while label filtering leads to a notable decrease in performance, ranking second in impact. Specifically, "W/O AGENT 1" achieves an ACC of 0.591 on the Cora dataset, representing a 12.2% decrease compared to MARK-MAGI. Meanwhile, the other variants, "W/O AGENT 2" and "W/O AGENT 3" experience smaller ACC reductions of 5.1% and 2.6%, respectively, on the same dataset. In conclusion, the removal of each agent individually causes varying degrees of performance degradation, underscoring the pivotal role each agent plays in the overall efficacy of the designed pipeline.

4.4 Sensitivity Analysis

In this section, we present a sensitivity analysis examining two critical aspects: (1) hyper-parameter sensitivity and (2) the impact of LLM selection on
<table>
  <tr>
    <th rowspan="2">Dataset</th>
    <th rowspan="2">Metric</th>
    <th colspan="3">DMoN</th>
    <th colspan="3">MARK-DMoN</th>
    <th colspan="3">MAGI</th>
    <th colspan="3">PRE-TRAIN MARK-MAGI IMP †</th>
  </tr>
  <tr>
    <th>ACC</th>
    <th>NMI</th>
    <th>ARI</th>
    <th>ACC</th>
    <th>NMI</th>
    <th>ARI</th>
    <th>ACC</th>
    <th>NMI</th>
    <th>ARI</th>
    <th>IMP</th>
    <th>IMP †</th>
  </tr>
  <tr>
    <td rowspan="4">CORA</td>
    <td>ACC</td>
    <td>0.628±0.080</td>
    <td>0.671±0.024</td>
    <td>0.675±0.017</td>
    <td>4.70%</td>
    <td>0.664±0.057</td>
    <td>0.697±0.018</td>
    <td><b>0.713±0.032</b></td>
    <td>4.90%</td>
  </tr>
  <tr>
    <td>NMI</td>
    <td>0.490±0.042</td>
    <td>0.515±0.011</td>
    <td>0.548±0.005</td>
    <td>5.80%</td>
    <td>0.529±0.010</td>
    <td>0.533±0.004</td>
    <td><b>0.578±0.007</b></td>
    <td>4.90%</td>
  </tr>
  <tr>
    <td>ARI</td>
    <td>0.437±0.069</td>
    <td>0.452±0.001</td>
    <td>0.473±0.014</td>
    <td>3.60%</td>
    <td>0.464±0.048</td>
    <td>0.489±0.003</td>
    <td><b>0.490±0.022</b></td>
    <td>2.60%</td>
  </tr>
  <tr>
    <td>F1</td>
    <td>0.565±0.072</td>
    <td>0.618±0.028</td>
    <td>0.632±0.026</td>
    <td>6.70%</td>
    <td>0.614±0.055</td>
    <td>0.644±0.035</td>
    <td><b>0.670±0.037</b></td>
    <td>5.60%</td>
  </tr>
  <tr>
    <td rowspan="4">CITESEER</td>
    <td>ACC</td>
    <td>0.628±0.023</td>
    <td>0.633±0.023</td>
    <td>0.646±0.016</td>
    <td>1.80%</td>
    <td>0.624±0.059</td>
    <td>0.650±0.055</td>
    <td><b>0.670±0.022</b></td>
    <td>4.60%</td>
  </tr>
  <tr>
    <td>NMI</td>
    <td>0.391±0.005</td>
    <td>0.419±0.008</td>
    <td>0.423±0.012</td>
    <td>3.20%</td>
    <td>0.411±0.028</td>
    <td>0.424±0.025</td>
    <td><b>0.438±0.009</b></td>
    <td>2.70%</td>
  </tr>
  <tr>
    <td>ARI</td>
    <td>0.384±0.013</td>
    <td>0.401±0.013</td>
    <td>0.409±0.017</td>
    <td>2.50%</td>
    <td>0.396±0.043</td>
    <td>0.418±0.039</td>
    <td><b>0.432±0.014</b></td>
    <td>3.60%</td>
  </tr>
  <tr>
    <td>F1</td>
    <td>0.598±0.016</td>
    <td>0.608±0.025</td>
    <td>0.620±0.012</td>
    <td>2.20%</td>
    <td>0.586±0.068</td>
    <td>0.616±0.059</td>
    <td><b>0.638±0.015</b></td>
    <td>5.20%</td>
  </tr>
  <tr>
    <td rowspan="4">WIKICS</td>
    <td>ACC</td>
    <td>0.326±0.019</td>
    <td>0.481±0.021</td>
    <td>0.520±0.032</td>
    <td>19.40%</td>
    <td>0.534±0.049</td>
    <td>0.552±0.037</td>
    <td><b>0.606±0.033</b></td>
    <td>7.20%</td>
  </tr>
  <tr>
    <td>NMI</td>
    <td>0.235±0.023</td>
    <td>0.396±0.016</td>
    <td>0.416±0.023</td>
    <td>18.10%</td>
    <td>0.467±0.018</td>
    <td>0.476±0.012</td>
    <td><b>0.493±0.010</b></td>
    <td>2.60%</td>
  </tr>
  <tr>
    <td>ARI</td>
    <td>0.090±0.016</td>
    <td>0.213±0.014</td>
    <td>0.258±0.044</td>
    <td>16.80%</td>
    <td>0.381±0.051</td>
    <td>0.405±0.039</td>
    <td><b>0.466±0.021</b></td>
    <td>8.50%</td>
  </tr>
  <tr>
    <td>F1</td>
    <td>0.255±0.023</td>
    <td>0.430±0.026</td>
    <td>0.463±0.035</td>
    <td>20.80%</td>
    <td>0.447±0.047</td>
    <td>0.457±0.039</td>
    <td><b>0.515±0.052</b></td>
    <td>6.80%</td>
  </tr>
  <tr>
    <td rowspan="4">PUBMED</td>
    <td>ACC</td>
    <td>0.495±0.026</td>
    <td>0.576±0.054</td>
    <td>0.613±0.001</td>
    <td>11.80%</td>
    <td>0.590±0.001</td>
    <td>0.589±0.001</td>
    <td><b>0.615±0.011</b></td>
    <td>2.50%</td>
  </tr>
  <tr>
    <td>NMI</td>
    <td>0.157±0.046</td>
    <td>0.194±0.035</td>
    <td>0.163±0.001</td>
    <td>0.60%</td>
    <td>0.180±0.001</td>
    <td>0.179±0.001</td>
    <td><b>0.198±0.018</b></td>
    <td>1.80%</td>
  </tr>
  <tr>
    <td>ARI</td>
    <td>0.140±0.058</td>
    <td>0.163±0.024</td>
    <td>0.179±0.001</td>
    <td>3.90%</td>
    <td>0.154±0.002</td>
    <td>0.152±0.001</td>
    <td><b>0.192±0.018</b></td>
    <td>3.80%</td>
  </tr>
  <tr>
    <td>F1</td>
    <td>0.462±0.031</td>
    <td>0.576±0.053</td>
    <td>0.608±0.001</td>
    <td>14.60%</td>
    <td>0.590±0.001</td>
    <td>0.589±0.001</td>
    <td><b>0.611±0.009</b></td>
    <td>2.10%</td>
  </tr>
</table>

Table 2: Clustering performance of MARK with two distinct backbones, DMoN and MAGI separately. The boldfaced scores represent the best results.

<table>
  <tr>
    <th rowspan="2">Dataset</th>
    <th rowspan="2">Metric</th>
    <th colspan="2">w/o agent 1</th>
    <th colspan="2">w/o agent 2</th>
    <th colspan="2">w/agent 3</th>
    <th colspan="2">MARK-MAGI</th>
  </tr>
  <tr>
    <th>ACC</th>
    <th>NMI</th>
    <th>ACC</th>
    <th>NMI</th>
    <th>ACC</th>
    <th>NMI</th>
    <th>ACC</th>
    <th>NMI</th>
  </tr>
  <tr>
    <td rowspan="4">CORA</td>
    <td>ACC</td>
    <td>0.591±0.052</td>
    <td>0.687±0.036</td>
    <td>0.662±0.046</td>
    <td><b>0.713±0.032</b></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>NMI</td>
    <td>0.482±0.029</td>
    <td>0.556±0.012</td>
    <td>0.544±0.020</td>
    <td><b>0.578±0.007</b></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>ARI</td>
    <td>0.346±0.038</td>
    <td>0.488±0.041</td>
    <td>0.428±0.036</td>
    <td><b>0.490±0.022</b></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>F1</td>
    <td>0.558±0.073</td>
    <td>0.646±0.037</td>
    <td>0.614±0.055</td>
    <td><b>0.670±0.037</b></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td rowspan="4">CITESEER</td>
    <td>ACC</td>
    <td>0.540±0.056</td>
    <td>0.638±0.070</td>
    <td>0.608±0.040</td>
    <td><b>0.670±0.022</b></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>NMI</td>
    <td>0.336±0.031</td>
    <td>0.414±0.041</td>
    <td>0.400±0.023</td>
    <td><b>0.438±0.009</b></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>ARI</td>
    <td>0.292±0.045</td>
    <td>0.409±0.058</td>
    <td>0.374±0.039</td>
    <td><b>0.432±0.014</b></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>F1</td>
    <td>0.500±0.055</td>
    <td>0.595±0.077</td>
    <td>0.570±0.056</td>
    <td><b>0.638±0.015</b></td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
</table>

Table 3: Ablation Study of MARK-MAGI on CORA and CITESEER Datasets.

MARK’s performance.

To assess the sensitivity of the proposed framework MARK to hyper-parameters, we conduct two analyses on the Cora and CiteSeer datasets. The first analysis examines the hyper-parameter associated with the number of high-confidence samples selected by the concept agent in each cluster, as these samples directly impact the quality of the cluster topics. The second analysis focuses on the hyper-parameter controlling the number of neighbors fed into the generation agent, which greatly influences the quality of the virtual text it synthesizes.

In the context of the concept agent, we investigate the effect of varying the number of high-confidence samples on the topic name derived by the agent, which subsequently influences the cluster assignments determined by the inference agent and ultimately impacts the model’s performance. As depicted in Figure 4, a limited number of samples may lead to inaccurate or incomplete topic representations, while an increasing number of samples facilitates the exploration of inter-sample similarities. However, the introduction of additional relationships within the group can cause the clusters to become fragmented, resulting in performance fluctuations that are sensitive to the hyper-parameter settings. As shown in Figure 5, it is observed that the inclusion of solely the uncertain text may significantly impact the quality of the newly generated text, thereby introducing greater uncertainty into MARK. Consequently, we propose generating virtual text that incorporates both the uncertain node and its surrounding neighborhood, leading to a notable performance improvement compared to the approach that disregards neighborhood semantics.

As illustrated in Table 4, we focus on the effect of LLM selection on the final performance of MARK-MAGI. We have incorporated two additional LLMs, GPT-3.5-turbo and Deepseek-R1, to examine the impact of LLM selection. Notably, all three LLMs clearly enhance clustering performance when integrated with ranking guidance, al-

![Sensitivity analysis regarding the number of high-confidence samples n for the concept agent. Panels (a) and (b) present the sensitivity analysis results on the Cora and CiteSeer datasets, respectively.](page_1012_1342_388_246.png)

Figure 4: Sensitivity analysis regarding the number of high-confidence samples n for the concept agent. Panels (a) and (b) present the sensitivity analysis results on the Cora and CiteSeer datasets, respectively.
Figure 5: Sensitivity analysis regarding the number of neighbors \( k \) for the generation agent. Panels (a) and (b) present the sensitivity analysis results on the Cora and CiteSeer datasets, respectively.

<table>
  <tr>
    <th>DATASET</th>
    <th>LLM</th>
    <th>ACC</th>
    <th>NMI</th>
    <th>ARI</th>
    <th>F1</th>
  </tr>
  <tr>
    <td rowspan="3">Cora</td>
    <td>GPT-3.5-TURBO</td>
    <td>0.714±0.029</td>
    <td>0.547±0.015</td>
    <td>0.502±0.029</td>
    <td><b>0.683±0.039</b></td>
  </tr>
  <tr>
    <td>GPT-4.0-MINI</td>
    <td>0.713±0.032</td>
    <td><b>0.578±0.007</b></td>
    <td>0.490±0.022</td>
    <td>0.670±0.037</td>
  </tr>
  <tr>
    <td>DEEPSEEK-R1</td>
    <td>0.707±0.034</td>
    <td>0.561±0.019</td>
    <td><b>0.509±0.036</b></td>
    <td>0.676±0.022</td>
  </tr>
  <tr>
    <td rowspan="3">CiteSeer</td>
    <td>GPT-3.5-TURBO</td>
    <td>0.660±0.051</td>
    <td>0.432±0.023</td>
    <td>0.427±0.038</td>
    <td>0.628±0.045</td>
  </tr>
  <tr>
    <td>GPT-4.0-MINI</td>
    <td><b>0.670±0.022</b></td>
    <td>0.438±0.009</td>
    <td>0.432±0.014</td>
    <td>0.638±0.015</td>
  </tr>
  <tr>
    <td>DEEPSEEK-R1</td>
    <td>0.666±0.036</td>
    <td>0.436±0.023</td>
    <td>0.431±0.032</td>
    <td>0.613±0.046</td>
  </tr>
</table>

Table 4: The impact of the chosen LLM on MARK-MAGI’s performance. The best results are highlighted in bold.

though the extent of improvement varies depending on the specific LLM, evaluation metrics, and datasets used.

4.5 Case Study

Concept Agent Reveals More Semantic Essence
We present cluster 3 from the CiteSeer dataset as a representative example, which is associated with Artificial Intelligence. However, we observe that node 1528, labeled as Machine Learning, emerges as the highest-confidence sample within cluster 3. This highlights a potential limitation: relying solely on the highest-confidence sample to represent a cluster may not accurately capture the cluster’s semantic essence. To address this, we feed the top-n confidence samples into the concept agent, and then provide a comprehensive summary of the cluster’s topic. For instance, when using the top-10 confidence samples, the concept agent assigns Probabilistic Models and Learning in Intelligent Systems to cluster 4. In contrast, when leveraging the top-200 samples, the derived topic name shifts to Probabilistic Reasoning and Machine Learning Techniques. Obviously, the latter aligns much better with the ground truth Machine Learning, demonstrating a larger sample size may yields a much more rich semantics.

Generation Agent Synthesizes Context-aware Texts
To better explore the quality of generated text considering neighborhood semantics, we take node 1383 from the CiteSeer dataset as a case study. Specifically, node 1383 is one of the uncertain nodes identifying by the graph clustering engine, labeled as Human Computer Interaction. As described in Table 9, we then compare the outcomes of two strategies for novel and virtual text generation: the former generates text based solely on the target node 1383 text, while the latter incorporates both the target and neighborhood texts. The target text presents a novel ASL recognition method, which aligns better with Human Computer Interaction and is somewhat related to Artificial Intelligence. The first generation prompt, focused on ASL frameworks, leads to a misleading predicted label of Artificial Intelligence. In contrast, the second prompt, utilizing the graph structure and neighborhood information, generates a text about gesture recognition, which is accurately labeled with Human Computer Interaction. In conclusion, including neighborhood texts in the generation prompt may contribute to higher quality text generation, providing a more effective and diverse perspective and enhancing text embeddings.

Inference Agent Filters low quality labels
Although the inference agent can predict labels for uncertain nodes by integrating both texts and contexts to some extent, there still exists some randomness and misclassification in the label predictions. Therefore, placing complete trust in the labels predicted by the inference agent could introduce additional noise into our pipeline. Instead, we filter out low-confidence labels by assessing the correspondence between the predicted label for the target text and the synthesized text. As shown in Table 10, the uncertain node 16 from the CiteSeer dataset is misclassified as Machine Learning by the inference agent, while its ground truth is Information Retrieval.

5 Conclusion

In this paper, we address the challenge of text-attributed graph clustering by leveraging the complementary strengths of graph neural networks (GNNs) and large language models (LLMs). Our proposed MARK is a novel framework that integrates GNNs with multi-agent LLM collaboration to generate robust, ranking-based guidance. To overcome the limitations of current approaches, such as narrow single-agent perspectives, insufficient noise filtering, and fragile supervision, we de-
sign three collaborative agents: the concept agent extracts cluster semantics, the generation agent synthesizes topology-aligned text for uncertain nodes, and the inference agent provides ranking-based feedback by contrasting uncertain and synthetic texts. By using consistent LLM feedback as supervisory signals and incorporating a ranking-aware loss, MARK effectively reduces noise interference and enhances clustering robustness. More importantly, extensive experiments validate that MARK offers a powerful pipeline for augmenting existing GNN-based clustering methods with LLMs.

6 Limitations

While the proposed MARK demonstrates promising performance in text-attributed graph clustering, an important limitation remains to be considered. Although topology alignment is enforced for generated texts, the framework does not explicitly verify factual consistency between synthetic texts and real-world knowledge. This may introduce subtle noise in scenarios requiring strict semantic fidelity.

7 Acknowledgments

This work was partially supported by the National Natural Science Foundation of China (Grant No. 32270689 and Grant No. 62176184).

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A  Prompts design for multi-agent framework

The detailed instructions for three agents, concept agent, generation agent and inference agent, are illustrated in Figure 6, Figure 7 and Figure 8.

# Concept Agent
You are an AI assistant specializing in text induction. Please generate a topic name based on the provided input texts.
Below are the high-confidence samples related to <dataset field> from a specific cluster. Analyze the commonalities and core content of these samples and provide a concise summary of the cluster's theme. Output the theme as a short name without adding any extra explanations. The cluster has the following high-confidence samples: <high-confidence texts of the i-th cluster>. Please comprehensively consider the commonalities between these samples and then conclude the topic of the cluster concisely. Output the newly generated topic name as a dictionary, with keys "answer" and "explanation".

Figure 6: The detailed instruction design of the concept agent for cluster induction.

# Generation Agent
You are an AI assistant specializing in text generation. Please create a virtual text based on the surrounding input texts.
Given a target article related to <dataset field>: <the text of the target article>. The topic of this article may fall under one of the following clusters: <topic name from Concept Agent>. It has the following important neighbors which have citation relationship to this article, from most related to least related: <the texts of Neighbors>. Please consider the information from the target article and its neighbors, and generate a virtual article similar to the given one, with a title of no more than 15 words and an abstract limited to 300 words. Output the newly generated virtual article as a dictionary, with keys "answer" and "explanation".

Figure 7: The detailed instruction design of the generation agent for neighbor summary.

# Inference Agent
You are an AI assistant specializing in text inference. Please identify the most likely cluster to which the given text belongs.
Given a target article related to <dataset field>: text. Please determine which cluster this <dataset field> most likely belongs to. The optional clusters are: <topic name from Concept Agent>. Please comprehensively consider which cluster this article most likely belongs to, only answer the cluster number directly as a dictionary, with keys "answer" and "explanation".

Figure 8: The detailed instruction design of the inference agent for decision filtration.

B  Evaluation Metrics

In our work, we adopt four widely recognized metrics: accuracy (ACC), normalized mutual information (NMI), adjusted rand index (ARI), and F1 score (F1), to comprehensively evaluate the clustering performance of MARK.

• ACC quantifies the alignment between predicted cluster labels and ground-truth labels. In the context of unsupervised clustering, the predicted clusters are first matched to the true labels using
the Hungarian algorithm. The value of ACC is then calculated from the resulting confusion matrix \( C \in \mathbb{R}^{K \times K} \) as follows:

\[
\text{ACC} = \frac{\sum_{i=1}^K C_{i,i}}{\sum_{i=1}^K \sum_{j=1}^K C_{i,j}},
\]

where \( C_{i,j} \) denotes the number of samples whose true label is \( i \) and predicted label is \( j \).

• **ARI** measures similarity between two assignments. Given a set S and two clustering results \( X = (X_1, X_2, ..., X_r) \) and \( Y = (Y_1, Y_2, ..., Y_s) \). Let \( C = \sum_{ij} \binom{n_{ij}}{2} \), \( D = \sum_i \binom{a_i}{2} \), \( E = \sum_j \binom{b_j}{2} \), where \( n_{ij} = |X_i \cap Y_j| \), \( a_i = \sum_j n_{ij} \), and \( b_j = \sum_i n_{ij} \). Then we can calculate ARI as follows:

\[
\text{ARI} = \frac{C - (D \cdot E)/\binom{n}{2}}{\frac{1}{2}(D + E) - (D \cdot E)/\binom{n}{2}}.
\]

• **NMI** calculates consistency between the predicted and true labels. Given two clustering results \( X = (X_1, X_2, ..., X_r) \) and \( Y = (Y_1, Y_2, ..., Y_s) \),

\[
\text{NMI} = \frac{I(X, Y)}{\max\{H(X), H(Y)\}},
\]

where \( I(X, Y) \) is the mutual information between \( X \) and \( Y \), \( H(X) \) and \( H(Y) \) are the entropy of \( X \) and \( Y \) respectively.

• **F1 Score** is computed as the arithmetic mean of the per-class F1 scores and is used to assess clustering performance. It balances precision and recall, providing a comprehensive measure of a model’s accuracy.

C Generated concepts

Here are three examples of the ground truth alongside the concepts generated by the concept agent.

<table>
  <tr>
    <th>Ground Truth</th>
    <th>Predicted Concept</th>
    <th>Explanation</th>
  </tr>
  <tr>
    <td>Reinforcement Learning</td>
    <td>Reinforcement Learning and Function Approximation</td>
    <td>The texts consistently discuss various aspects and techniques of reinforcement learning, particularly focusing on the challenges and methodologies associated with integrating function approximation methods...</td>
  </tr>
  <tr>
    <td>Case Based</td>
    <td>Adaptive Case-Based Reasoning Systems</td>
    <td>The samples in this cluster revolve around the development and enhancement of case-based reasoning systems, focusing on adaptability, learning from past experiences, and implementing various mechanisms...</td>
  </tr>
  <tr>
    <td>Rule Learning</td>
    <td>Probabilistic Reasoning and Decision-Making</td>
    <td>The cluster focuses on the development and application of probabilistic models...</td>
  </tr>
  <tr>
    <td>Probabilistic Methods</td>
    <td>Bayesian Methods and Applications</td>
    <td>The samples in this cluster focus on various aspects of Bayesian statistical methods, including parameter estimation, model comparison, posterior distributions, and applications of Markov Chain Monte Carlo (MCMC)...</td>
  </tr>
  <tr>
    <td>Theory</td>
    <td>Adaptive Learning and Inductive Bias</td>
    <td>The cluster focuses on various machine learning techniques that emphasize the importance of learning algorithms’ adaptability...</td>
  </tr>
  <tr>
    <td>Genetic Algorithms</td>
    <td>Evolutionary Algorithms in Problem Solving</td>
    <td>The cluster highlights the development and application of evolutionary algorithms, particularly genetic algorithms and genetic programming, in solving complex computational problems across various domains...</td>
  </tr>
  <tr>
    <td>Neural Networks</td>
    <td>Neural Network Theory and Applications</td>
    <td>The cluster comprises diverse studies focusing on the theoretical underpinnings, optimization, architectures, and various applications of neural networks...</td>
  </tr>
</table>

Table 5: Concepts generated by the concept agent on Cora dataset.
<table>
  <tr>
    <th>Ground Truth</th>
    <th>Predicted Concept</th>
    <th>Explanation</th>
  </tr>
  <tr>
    <td>Agents</td>
    <td>Multi-Agent System Architectures and Methodologies</td>
    <td>The cluster is characterized by a focus on various aspects of multi-agent systems including design methodologies, architectures, collaboration mechanisms, and the handling of complex interactions among agents...</td>
  </tr>
  <tr>
    <td>Machine Learning</td>
    <td>Probabilistic Learning and Decision Systems</td>
    <td>The cluster comprises various papers focusing on the integration of probabilistic frameworks and learning algorithms across multiple domains, including artificial intelligence, machine learning...</td>
  </tr>
  <tr>
    <td>Information Retrieval</td>
    <td>Intelligent Information Retrieval and Web Search Techniques</td>
    <td>The cluster encompasses diverse research on advanced methodologies and algorithms for enhancing information retrieval and search capabilities on the web...</td>
  </tr>
  <tr>
    <td>Database</td>
    <td>Query Optimization and Maintenance in Data Warehousing</td>
    <td>The cluster focus on various techniques and methodologies for optimizing query performance and maintaining data integrity within data warehousing systems...</td>
  </tr>
  <tr>
    <td>Human Computer Interaction</td>
    <td>Ubiquitous Computing and Interactive Interfaces</td>
    <td>The cluster focus on enhancing human-computer interaction through ubiquitous computing, mobile devices, and augmented reality...</td>
  </tr>
  <tr>
    <td>Artificial Intelligence</td>
    <td>Vision-Based Learning and Interaction</td>
    <td>The cluster encompasses various approaches and systems involving vision-based techniques for obstacle detection, navigation, gesture recognition, and human-computer interaction...</td>
  </tr>
</table>

Table 6: Concepts generated by the concept agent on CiteSeer dataset.

<table>
  <tr>
    <th>Ground Truth</th>
    <th>Predicted Concept</th>
    <th>Explanation</th>
  </tr>
  <tr>
    <td>Computational linguistics</td>
    <td>Multilingual Terminology and Language Processing</td>
    <td>The samples revolve around terminological databases and technologies related to language processing and translation across multiple languages...</td>
  </tr>
  <tr>
    <td>Databases</td>
    <td>Classic Video Game Developments</td>
    <td>The cluster comprises detailed accounts of various classic video games...</td>
  </tr>
  <tr>
    <td>Operating systems</td>
    <td>Live Operating Systems and Unix Variants</td>
    <td>This cluster is composed of various texts that revolve around live operating systems...</td>
  </tr>
  <tr>
    <td>Computer architecture</td>
    <td>Microprocessor Architectures and Technologies</td>
    <td>The cluster encompasses a variety of microprocessor architectures, including RISC, CISC, and several proprietary designs...</td>
  </tr>
  <tr>
    <td>Computer security</td>
    <td>Emerging Cybersecurity Threats and Defenses</td>
    <td>The texts are related to various aspects of cybersecurity, including vulnerabilities, attack methodologies...</td>
  </tr>
  <tr>
    <td>Internet protocols</td>
    <td>Network Protocols and Management</td>
    <td>The cluster consists of a wide range of texts detailing various network protocols...</td>
  </tr>
  <tr>
    <td>Computer file systems</td>
    <td>Evolution of Windows Operating Systems</td>
    <td>The cluster contains detailed descriptions and features related to various iterations of Microsoft’s Windows operating systems...</td>
  </tr>
  <tr>
    <td>Distributed computing architecture</td>
    <td>Service-Oriented Architectures and Integration Technologies</td>
    <td>The samples collectively emphasize concepts and technologies related to service-oriented architectures (SOA), integration frameworks, and data sharing methodologies...</td>
  </tr>
  <tr>
    <td>Web technology</td>
    <td>Mobile Operating Systems and Devices</td>
    <td>The provided texts explore detailed information on various mobile operating systems (like iOS and Android)...</td>
  </tr>
  <tr>
    <td>Programming language topics</td>
    <td>Programming Languages and Their Implementations</td>
    <td>The provided samples focus on various programming languages, their features, implementation techniques...</td>
  </tr>
</table>

Table 7: Concepts generated by the concept agent on WikiCS dataset.

D Computational Cost and Runtime Analysis

MARK introduces a novel paradigm to enhance conventional graph clustering methods by incorporating multi-agent ranking guidance. Specifically, we select two clustering methods, DMoN and MAGI, as the graph clustering engine separately to identify uncertain node texts, which are then processed by an LLM and fine-tuned through multi-agent ranking guidance. Consequently, the computational cost and runtime difference between MARK and its backbone methods stems from the execution of multi-agent module.
Table 8 presents the computational cost and running time of MARK-MAGI when using the API (based on GPT-4o-mini). Following the approach of TAPE (He et al., 2023), we estimate both cost and runtime based on the number of input and output tokens.

<table>
  <tr>
    <th>Dataset</th>
    <th>Cora</th>
    <th>CiteSeer</th>
    <th>WikiCS</th>
    <th>PubMed</th>
  </tr>
  <tr>
    <th>Cost($)</th>
    <td>0.25</td>
    <td>0.18</td>
    <td>0.55</td>
    <td>0.53</td>
  </tr>
  <tr>
    <th>Runtime(min)</th>
    <td>3.79</td>
    <td>2.79</td>
    <td>12.15</td>
    <td>13.46</td>
  </tr>
</table>

Table 8: Computational cost and runtime analysis.

E   Case Study

Factual Accuracy of Generated Texts   To evaluate the quality of the generated texts, we use the ROUGE metric (Lin, 2004), where the score ranges from 0 to 1, with a higher score indicating greater factual alignment between the generated and referenced texts. Specifically, we calculate the ROUGE score between the generated texts and the referenced target node text, as well as its similar neighboring texts. For comparison, we also compute the ROUGE score between randomly sampled texts and the same referenced texts. On the Cora, CiteSeer, and PubMed datasets, the ROUGE scores for the LLM-generated texts are 0.39, 0.37, and 0.50, respectively, while the scores for the randomly sampled texts are 0.25, 0.25, and 0.27.

Case Study of Generated Texts   Here, we present two case studies to illustrate the quality of the generated texts. Table 9 shows a comparison between generated texts with and without incorporating neighborhood texts. Table 10 demonstrates how label filtering, guided by ranking, helps prevent the introduction of additional noise into the graph clustering engine.

Target Text.
Title: Toward Scalability in ASL Recognition: Breaking Down Signs into Phonemes.
Abstract: In this paper we present a novel approach to continuous, whole-sentence <i>ASL recognition</i> that uses phonemes instead of whole signs as the basic units... We subsequently train <b>Hidden Markov Models (HMMs)</b> to recognize the phonemes...
Ground Truth: Human Computer Interaction.
Predicted Label: Artificial Intelligence.
Explanation: This falls under Vision-Based Learning and Interaction as it involves recognizing and interpreting visual gestures within a computational framework.

Text generated based solely on the target text.
Title: Enhancing ASL Interpretation Through Phoneme-Based Recognition Frameworks.
Abstract: In this paper, we introduce an innovative framework for the interpretation of American Sign Language (ASL) by focusing on <i>phoneme-level decomposition</i> rather than traditional sign recognition...We utilize <b>advanced Hidden Markov Models (HMMs)</b> to train our system in recognizing these phonemes...
Predicted Label: Artificial Intelligence.
Explanation: The use of Hidden Markov Models also indicates an emphasis on computational techniques related to vision-based systems.

Text generated based on both the target and adjacent texts.
Title: Improving Continuous Gesture Recognition with Gesture Segmentation and Classification.
Abstract: In this research, we introduce an innovative framework for <i>continuous gesture recognition</i> that emphasizes the segmentation of gestures into smaller, classifiable units known as gesture segments...
Predicted Label: Human Computer Interaction.
Explanation: The article focuses on improving continuous gesture recognition, which is directly related to human-computer interaction.

Table 9: Comparison of generated text quality with and without considering neighborhood texts.

F   Generalization Across Backbones and Datasets

To better demonstrate the generalizability and applicability of MARK, we extend it to text-rich graphs in other domains, such as e-commerce networks. Furthermore, we incorporate additional deep graph clustering frameworks, namely Dink-Net (Liu et al., 2023a) and HSAN (Liu et al., 2023b), as backbones to further explore the potential of MARK in enhancing deep graph clustering through this novel paradigm.
Target Text.
Title: Error-Driven Pruning of Treebank Grammars for Base Noun Phrase.
Abstract: Identification Finding simple, non-recursive, base noun phrases is an important subtask for many natural language processing applications...
Ground Truth: Information Retrieval.
Predicted Label: Machine Learning
Explanation: The article relates to natural language processing (NLP) and discusses algorithms for identifying base noun phrases, which involves machine learning methods.

Generated text by generation agent.
Title: Streamlined Algorithms for Identifying Core Verb Phrases in Textual Data.
Abstract: Identifying core verb phrases within textual data is a foundational task in various natural language processing (NLP) applications...we introduce a streamlined algorithm specifically designed to accommodate the simpler nature of identifying core verb phrases...
Predicted Label: Information Retrieval.
Explanation: The article focuses on natural language processing (NLP) applications, specifically identifying core verb phrases in textual data, which is closely related to intelligent information retrieval.

Table 10: Label filtering based on consistency between target text label and generated text label

Generalization Across Backbones   MARK introduces a paradigm to enhance deep graph clustering methods by incorporating multi-agent ranking guidance. In our paper, we use DMoN and MAGI as baselines, but other deep graph clustering frameworks can also serve as backbones. To further explore this, we have additionally incorporated Dink-Net (Liu et al., 2023a) and HSAN (Liu et al., 2023b) from your references as graph clustering engines. Their performance on the Cora dataset is presented below:

<table>
  <tr>
    <th>Metric</th>
    <th>DINK-NET</th>
    <th>PRE-TRAIN</th>
    <th>MARK-DINK-NET IMP ↑</th>
    <th>HSAN</th>
    <th>PRE-TRAIN</th>
    <th>MARK-HSAN IMP ↑</th>
  </tr>
  <tr>
    <td>ACC</td>
    <td>0.557±0.029</td>
    <td>0.613±0.053</td>
    <td>0.669±0.020</td>
    <td>11.2%</td>
    <td>0.646±0.018</td>
    <td>0.671±0.023</td>
    <td>0.676±0.046</td>
    <td>3.0%</td>
  </tr>
  <tr>
    <td>NMI</td>
    <td>0.434±0.012</td>
    <td>0.516±0.013</td>
    <td>0.517±0.006</td>
    <td>8.3%</td>
    <td>0.511±0.014</td>
    <td>0.525±0.010</td>
    <td>0.544±0.019</td>
    <td>3.3%</td>
  </tr>
  <tr>
    <td>F1</td>
    <td>0.502±0.040</td>
    <td>0.561±0.042</td>
    <td>0.590±0.035</td>
    <td>8.8%</td>
    <td>0.593±0.035</td>
    <td>0.621±0.023</td>
    <td>0.638±0.044</td>
    <td>4.5%</td>
  </tr>
</table>

Table 11: Clustering performance of MARK-Dink-Net and MARK-HSAN.

Generalization Across Datasets   We evaluate MARK on the e-commerce network Books-History (Yan et al., 2023), which consists of 41,551 nodes, 358,474 edges, and 12 classes. Specifically, the node attributes represent book descriptions, while the edges indicate that two books are co-purchased or co-viewed. The performance of MARK is presented in the following table:

<table>
  <tr>
    <th>Metric</th>
    <th>MAGI</th>
    <th>PRE-TRAIN</th>
    <th>MARK-MAGI IMP ↑</th>
  </tr>
  <tr>
    <td>ACC</td>
    <td>0.341±0.024</td>
    <td>0.362±0.012</td>
    <td>0.367±0.015</td>
    <td>2.6%</td>
  </tr>
  <tr>
    <td>NMI</td>
    <td>0.326±0.010</td>
    <td>0.338±0.002</td>
    <td>0.341±0.006</td>
    <td>1.5%</td>
  </tr>
  <tr>
    <td>ARI</td>
    <td>0.153±0.010</td>
    <td>0.165±0.011</td>
    <td>0.207±0.014</td>
    <td>5.4%</td>
  </tr>
</table>

Table 12: Clustering performance of MARK on Books-History datasets.

Reviewer paper summaries: This paper introduces a multi-agent system that enhances text-attributed graph clustering by combining graph neural network (GNN)-based representations with large language model (LLM)-driven concept induction, synthetic text generation, and label inference.

The key idea is to detect nodes with ambiguous cluster assignments, generate topology-consistent synthetic text, and validate node labels by comparing them to their synthetic counterparts for a more robust fine-tuning stage.

Experiments demonstrate that a ranking-guided supervision process achieves improved clustering performance while addressing noise in LLM-derived labels.
Summary of strengths: 1. The submission effectively demonstrates how LLMs could provide meaningful semantic supervision for unsupervised graph clustering tasks. Rather than relying on LLMs solely for encoding or direct classification, collaborative agents are used to create ranking-based supervision signals, providing a fresh perspective in applying LLMs to sophisticated graph tasks.
2. The proposed approach specifically addresses the critical challenge of uncertain or boundary nodes, utilising synthetic text generation and ranking guidance to fine-tune clustering models. This enhances model robustness against noisy labels, an essential contribution relevant to real-world graph data applications.
3. Experiments conducted across multiple benchmark datasets (Cora, CiteSeer, PubMed, WikiCS) validate the main claims to an extent. The ablation studies and sensitivity analyses further deepen the empirical insights, thoroughly justifying design choices.
Summary of weaknesses: 1. Though experiments on standard benchmarks (Cora, CiteSeer, PubMed, WikiCS) are thorough, these datasets represent academic-article graphs. This raises some concern about how readily the approach generalizes to text-rich graphs in other domains (e.g., social media data or e-commerce networks), leaving the broader applicability uncertain without further demonstration.
2. The approach relies heavily on generated synthetic texts from LLMs, but the realism or accuracy of these synthetic texts (especially in specific domains) may vary greatly. Addressing this explicitly could improve confidence in the practical reliability of the method.
Comments, suggestions and typos: 1. Add a brief subsection, analysing the time and space complexity and quantifying computational overhead (e.g., GPU hours, inference latency) compared to baseline methods. Even a rough approximation or discussion will provide important practical context.
2. Clearly explain why several methods in deep graph clustering, referring to surveys [1,2], were not used as baselines in this submission.

References:

[1] An Overview of Advanced Deep Graph Node Clustering,  IEEE Transactions on Computational Social Systems, 2024,

[2] A Survey of Deep Graph Clustering: Taxonomy, Challenge, and Application, arXiv.2211.12875.
Overall assessment scores: 3.5
Metareview score: 3.5
Metareview: This paper addresses text-attributed graph clustering by leveraging large language models (LLMs) to overcome challenges near cluster boundaries due to label scarcity. The proposed MARK framework uses a multi-agent collaboration approach with ranking guidance, combining coarse clustering, synthetic text generation, and ranking-based supervision to refine clustering performance, outperforming competing baselines on benchmark datasets. In general, all reviewers were positive to accept this paper as least in the findings track.
Summary of reasons to publish: 1. This paper studies an interesting and meaningful problem.
2. The novelty of this work was identified by reviewers.
3. This paper was well written and organized.
4. The proposed framework seems to be with good practical impact.