Title: Hypergraph-Enhanced Contrastive Learning for Multi-View Clustering with Hyper-Laplacian Regularization
Venue: NeurIPS 2025
Accepted: yes
Keywords: Multi-view Clustering
Abstract: Deep multi-view clustering (DMVC) has emerged as a promising paradigm for integrating information from multiple views by leveraging the representation power of deep neural networks. However, most existing DMVC methods primarily focus on modeling pairwise relationships between samples, while neglecting higher-order structural dependencies among multiple samples, which may hinder further improvements in clustering performance. To address this limitation, we propose a hypergraph neural network (HGNN)-driven multi-view clustering framework, termed Hypergraph-enhanced cOntrastive learning with hyPEr-Laplacian regulaRization (HOPER), a novel model that jointly captures high-order correlations and preserves local manifold structures across views. Specifically, we first construct view-specific hypergraph structures and employ the HGNN to learn node representations, thereby capturing high-order relationships among samples. Furthermore, we design a hypergraph-driven dual contrastive learning mechanism that integrates inter-view contrastive learning with intra-hyperedge contrastive learning, promoting cross-view consistency while maintaining discriminability within hyperedges. Finally, a hyper-Laplacian manifold regularization is introduced to preserve the local geometric structure within each view, thereby enhancing the structural fidelity and discriminative power of the learned representations. Extensive experiments on diverse datasets demonstrate the effectiveness of our approach.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work proposes a hypergraph neural network-based framework for deep multi-view clustering. The method addresses the limitation of existing approaches that focus primarily on pairwise relationships by introducing view-specific hypergraphs to model high-order sample dependencies. It further incorporates a dual contrastive learning strategy to enhance both cross-view consistency and intra-hyperedge discriminability. In addition, a hyper-Laplacian regularization term is employed to preserve local geometric structures within each view. Together, these components contribute to improved clustering performance through more comprehensive structural representation.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths：\n1. The paper introduces view-specific hypergraphs to capture high-order sample relationships, addressing limitations of pairwise multi-view clustering.\n2. A novel inter-view and intra-hyperedge contrastive learning framework enhances cross-view consistency and within-hyperedge discriminability.\n3. Experiments on multiple benchmarks show superior performance over recent methods, supported by visualizations that aid interpretability.\n\nWeaknesses:\n1. The introduction lacks adequate clarification of some necessary concepts, making certain sections difficult to understand. For instance, when the authors mention using hypergraphs to model high-order correlations, it is unclear whether these high-order correlations refer to inter-view or intra-view relationships.\n2. Section 3.3 lacks a clear explanation of the motivation behind the Hypergraph-enhanced Dual Contrastive Learning. The phrase “treating them as positives” is ambiguous, as it is unclear what “them” specifically refers to. Clarifying this point would help improve understanding.\n3. The single line of ``Input'' in the pseudocode is insufficient, as it omits the hyperparameters \\(\\lambda_1\\) and \\(\\lambda_2\\).\n4. Some experimental settings are unclear. For example, the parameter configurations in Section 4.1 (“Implementation details”) are inconsistent with the hyperparameter values reported in Section 4.3, and no explanation is provided for this discrepancy.\n\nQUESTIONS:\nSee Weaknesses.\n\nLIMITATIONS:\nNo discussion of limitations or societal risks is provided. It would be beneficial for the authors to acknowledge any potential drawbacks or broader consequences of the proposed method.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-06-27"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe manuscript addresses a key limitation of current deep multi-view clustering methods that overlook higher-order sample interactions by focusing only on pairwise relations. To tackle this, the authors leverage hypergraph neural networks combined with a dual contrastive learning strategy to improve cross-view alignment and intra-hyperedge discrimination. The integration of hyper-Laplacian regularization further preserves local manifold structures. Experimental results on multiple benchmarks validate the method’s superior clustering performance.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths：\n\n(1) The manuscript is well-structured, and the overall logic is rigorous and coherent.\n\n(2) The use of high-order structural priors and contrastive learning to facilitate clustering-friendly representation learning is a novel and interesting idea.\n\n(3) The experimental setup is clearly presented, and the proposed method achieves better performance than recent multi-view clustering approaches.\n\nWeaknesses:\n\n(1) The description of the dual contrastive losses is insufficient and requires further clarification.\n\n(2) The descriptions of some equations are insufficient; specifically, the temperature parameters in Equations (5) and (6) lack clarity regarding whether they are shared or separately optimized. \n\n(3) There are formatting inconsistencies, including mixed italic and upright subscripts in Equations (5) and (6), as well as inconsistent table header styles.\n\nQUESTIONS:\n(1) The definitions and roles of inter-view and intra-view contrastive losses are not clearly articulated. It remains unclear how each specifically contributes to cross-view consistency and within-view discriminability.\n\n(2) Equation (7) lacks a detailed explanation of the construction of the hypergraph Laplacian matrix and the mechanism behind the hyper-Laplacian regularization term.\n\n(3) Figure 2 indicates that the proposed method exhibits stable performance on some datasets but fluctuates significantly on others. The manuscript should discuss potential reasons for this variation in robustness.\n\n(4) While hypergraph construction is intended to capture high-order relations to enhance representation learning, the underlying mechanism is not sufficiently explained and would benefit from a more in-depth discussion.\n\nLIMITATIONS:\nA potential limitation of hypergraph modeling is the increased computational complexity associated with capturing high-order relationships. This aspect should be explicitly acknowledged and discussed in the manuscript.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-28"}

comments: SUMMARY:
In this work, the authors propose a hypergraph-enhanced contrastive learning method for multi-view clustering. The method leverages hypergraph structures and a weight-sharing hypergraph neural network to capture high-order data correlations. In addition, it introduces an inter-view sample discriminability enhancement strategy, which effectively alleviates the issue of weak discriminability caused by information propagation in hypergraph neural networks. Extensive experiments validate the feasibility and effectiveness of the proposed model.

STRENGTHS AND WEAKNESSES:
Strengths：
1. Introducing hypergraph neural networks into multi-view clustering effectively captures high-order data correlations. This idea is highly innovative.

2. The simultaneous use of inter-view and intra-view contrastive losses significantly enhances the discriminability of learned sample representations, which is highly beneficial for clustering tasks.

3.  A series of experiments, including ablation studies, thoroughly validate the superior clustering performance of the proposed model.

Weaknesses：

1. The detailed process of hypergraph construction is missing.

2. In Equation (9), there seems to be a typographical error in the size of the variable P.

3. In Figure 2, the font size of the axis labels and tick marks is too small.

4. The manuscript lacks convergence experiments.

5. Compared with pre-training followed by fine-tuning, how does the clustering performance of the jointly optimized approach compare?

QUESTIONS:
Please see Weaknesses.

LIMITATIONS:
This work is a fundamental study in deep clustering and does not exhibit obvious limitations.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 4
DATE: 2025-06-30

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes HOPER, a novel multi-view clustering framework that integrates hypergraph-enhanced contrastive learning with hypergraph Laplacian regularization to learn discriminative feature representations.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths：\n1. The structure of this paper is great.\n2. The experiments are comprehensive.\n3. The motivation is clear.\n\nWeaknesses:\n1. The novelty is limited: Hypergraph Contrastive Learning and Hyper-Laplacian Regularization have all been proposed in previous studies.\n2. Figure 2 is less informative.\n\nQUESTIONS:\n1, Hypergraph Contrastive Learning has been proposed in: \"Multi-view Mixed Attention for Contrastive Learning on Hypergraphs. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval\", \"Multi-view Hypergraph Adaptive Contrastive Learning. In Pacific-Asia Conference on Knowledge Discovery and Data Mining\", \"Multi-view hypergraph contrastive policy learning for conversational recommendation. In Proceedings of the 46th international ACM SIGIR conference on research and development in information retrieval\", \"Cross-view graph contrastive learning with hypergraph. Information Fusion, 99, 101867.\"\nThe authors should clarify the differences between their method and other methods.\n2. Hyper-Laplacian Regularization has also been proposed in: \"Hyper-Laplacian regularized multi-view clustering with exclusive L21 regularization and tensor log-determinant minimization approach. ACM Transactions on Intelligent Systems and Technology, 14(3), 1-29\", \"Hyper-Laplacian Regularized Concept Factorization in Low-Rank Tensor Space for Multi-View Clustering. IEEE Transactions on Emerging Topics in Computational Intelligence\", \"Hyper-Laplacian regularized multi-view subspace clustering with low-rank tensor constraint. Neural Networks, 125, 214-223\", \"Hyper-Laplacian regularized nonconvex low-rank representation for multi-view subspace clustering. IEEE Transactions on Signal and Information Processing over Networks, 8, 376-388.\"\n The authors should also clarify the differences between their method and other methods.\n3. In summary, the two main contributions have been proposed in previous methods. The authors should clarify that their method is not a simple combination of existing methods. Moreover, these methods should also be compared in experiments.\n\nLIMITATIONS:\n1. The novelty is limited: Hypergraph Contrastive Learning and Hyper-Laplacian Regularization have all been proposed in previous studies.\n2. The comparisons have not included previous methods using Hypergraph Contrastive Learning and Hyper-Laplacian Regularization.", "TITLE": "Official Review", "RECOMMENDATION": 2, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 1, "DATE": "2025-07-03"}