Title: Hybrid-Collaborative Augmentation and Contrastive Sample Adaptive-Differential Awareness for Robust Attributed Graph Clustering
Venue: NeurIPS 2025
Accepted: yes
Keywords: Attributed graph clustering

contrastive learning

hybrid-collaborative representation augmentation

adaptive-differential sample awareness
Abstract: Due to its powerful capability of self-supervised representation learning and clustering, contrastive attributed graph clustering (CAGC) has achieved great success, which mainly depends on effective data augmentation and contrastive objective setting. However, most CAGC methods utilize edges as auxiliary information to obtain node-level embedding representation and only focus on node-level embedding augmentation. This approach overlooks  edge-level embedding augmentation and the interactions between node-level and edge-level embedding augmentations across various granularity. Moreover, they  often  treat all contrastive sample pairs equally, neglecting the significant differences between hard and easy positive-negative sample pairs, which ultimately limits their discriminative capability. To tackle these issues, a novel robust attributed graph clustering (RAGC), incorporating hybrid-collaborative augmentation (HCA) and contrastive sample adaptive-differential awareness (CSADA), is proposed. First, node-level and edge-level embedding representations and augmentations are simultaneously executed to establish a more comprehensive similarity measurement criterion for subsequent contrastive learning. In turn, the discriminative similarity  further consciously guides edge augmentation. Second, by leveraging pseudo-label information with high confidence, a CSADA strategy is elaborately designed, which adaptively identifies all contrastive sample pairs and differentially treats them by an innovative weight modulation function. The HCA and CSADA modules mutually reinforce each other in a beneficent cycle, thereby enhancing discriminability in representation learning. Comprehensive graph clustering evaluations over six benchmark datasets demonstrate the effectiveness of the proposed RAGC against several state-of-the-art CAGC methods. The code of RAGC could be available at https://github.com/TianxiangZhao0474/RAGC.git.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn the paper, the authors propose an unsupervised graph clustering method RAGC to improve the clustering performance, through combining two modules Hybrid-Collaborative Augmentation (HCA) and Contrastive Sample Adaptive-Differential Awareness (CSADA). The HCA module combines the dual collaborative augmentation of node-level and edge-level, and the CSADA module utilizes some dynamic prior information to differentiate the importance of contrastive sample pairs with an elaborate weight function from four key perspectives, eventually improving the discriminability of self-supervised graph embedding representation learning. The experimental results show significantly performance than existing methods on benchmark datasets. The theoretical analysis is sound and experimental verification is convining.\n\nSTRENGTHS AND WEAKNESSES:\nThe strengths:\nS1. To my knowledge, this is the first work to systematically categorize and distinguish the contrastive samples from four key criteria.\n\nS2. The work enhances the granularity and effectiveness of contrastive representation learning, and the  design is reasonable and interesting for general contrastive learning.\n\nS3. The experiments are convincing. The overall quality of the paper is high and has potential for acceptance.\n\nThe weakness: \nW1. This paper shows a relatively good readability, but there are still some writing and layout issues. \n\nW2. The paper does not provide a detailed analysis of the impact of the quality of pseudo-label information on the performance of RAGC.\n\nQUESTIONS:\nQ1: The CSADA module is dependent on the accuracy of pseudo-labeling in the contrastive sample weighting process, but the paper lacks a systematic analysis of the impact of pseudo-labeling errors on model performance. It is recommended to supplement the relevant analysis so as to enhance the completeness and persuasiveness of the paper.\n\nQ2. The dynamic pseudo-label selection mechanism is one of the innovations of this study, but how exactly does it dynamically change and converge during the training process?\n\nQ3. Sensitivity experiments on the weight adjustment parameters β and γ show that they are robust over a range, but how are the parameter ranges chosen? Please explain the selection principle, or provide a theoretical or empirical evidence.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper develops a RAGC framework to systematically improve representation discrimination in the contrastive graph clustering task by combining Hybrid-Collaborative Augmentation and Contrastive Sample Adaptive-Differential Awareness. The HCA module augments both node-level and edge-level features to achieve multi-granularity information fusion. The CSADA module dynamically adjusts the importance of different contrastive samples according to confidence level of pseudo-label and semantic similarity, thereby realizing differential learning between positive, negative, easy, and hard samples. Experiments verify the superior performance of RAGC on multiple datasets.\n\nSTRENGTHS AND WEAKNESSES:\nThe study proposes targeted improvements in both node-edge dual augmentation and contrastive sample differential perception, with good innovations and technical contributions. The experimental part is fully designed and validated, and the corresponding results are convincing over several representative methods. In addition, this paper also verifies the generalization of key module in other graph contrastive clustering methods, which is very interesting and valuable. More details of existing weakness can be found in the Questions section.\n\nQUESTIONS:\n1. In the HCA module of the model, why was the parameter-unshared MLP chosen to implement the data augmentation? If it is an empirical choice, it needs to be explained. Are there other, more efficient structures for enhancing the representation of graph data?\n2. The experiments were limited to homogeneous graph data (e.g., citation networks), and the generalization ability of the method on heterogeneous should be verified.\n3. In the HCA module, how do the node-level embedding augmenter and edge-level embedding augmenter collaborate?\n4. The CSADA module relies on pseudo-labels for sample weighting, but do different pseudo-label initialization strategies have an impact on the final clustering performance? The authors are requested to further explain the specific methodology and selection basis of the current pseudo-label initialization, and to indicate whether the process has better stability under various initialization.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis study proposes a novel graph contrastive clustering framework, RAGC, consisting of two crucial modules, HCA and CSADA. This proposed RAGC method simultaneously utilizes node embedding representation and edge embedding representation for comprehensive contrastive learning, and distinguishes different types of positive and negative sample pairs through an adaptive weighting mechanism, thus enhancing the discriminability and robustness of representation learning and clustering results. The experimental settings are sufficiently reasonable and it shows better performance than existing mainstream methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\nS1: The study introduces edge-level augmentation to fully utilize the structural information with a complementary and collaborative way, which aims at improving the completeness of representation learning and contrastive learning;\n\nS2: The sample weighting mechanism takes into account the differences between positive and negative samples as well as easy and difficult samples, which significantly improves the contrastiveness of self-supervised representation learning;  \n\nS3: The CSADA module has strong generalizability and can be potentially integrated into other contrastive learning frameworks with migration and extension value.\n\nWeaknesses\n\nW1: The scalability of this method for large-scale data needs further verification.\n\nQUESTIONS:\nQ1: The numerical significance of Fig.3 is unclear when expressing the ablation results.\n\nQ2: The dynamic confidence selection mechanism gradually expands the set of high-confidence samples during the training process, is this strategy still applicable when the sample distribution is extremely unbalanced at the beginning of training or when there is a high proportion of noisy samples?\n\nQ3: In CSADA module, confidence parameter $\\tau$ is adaptive and varies with the  training epoch. What are the advantages of this setting?\n\nQ4: Why did CSADA use positive/negative and easy/hard divisions rather than finer grained or other division criteria when it differentiated the sample into four categories for weighting?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-01"}

comments: SUMMARY:
The paper explores the robust attributed graph clustering and discusses two problems faced by existing works, i.e., insufficient data augmentation and rough differential perception of contrastive sample pairs in contrastive learning. To tackle these issues, a novel robust attributed graph clustering (RAGC), incorporating hybrid12 collaborative augmentation (HCA) and contrastive sample adaptive-differential awareness (CSADA), is proposed in this work, which integrates CSADA and HCA for better feature extraction capacities. The paper indicates its novelty and signifcance, which is also demontrated via a series of experiments.

STRENGTHS AND WEAKNESSES:
## Pros
1. The work is overall well-written with clear problem statement and motivations. The method is well illustrated.
2. The method design is technically solid, by employing CSADA as a reasonable and fine-grained extensions in differentiation awareness between positive and negative sample pairs.
3. The experiments are sufficient.

## Cons
1. Some details to be further explained. For example, the reason for adopting exponential weighting strategies.

QUESTIONS:
1. How about the adaptability of the work to heterogeneous graphs? I recommend to discuss this issue in brief.
2. The augmentation strategies for nodes and edges are from different feature sources, which might result in some problems of conflicting objectives. Please have a discussion on this issue.

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 4
ORIGINALITY: 4
DATE: 2025-07-03