Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: This paper proposes a multi-view clustering model, named MCGC. It utilizes some modern techniques, such as graph filtering, graph learning and contrastive learning, to successfully deal with multiple graphs data with multi-view attributes.
main_review: The main focus is on multiple graphs data with multi-view attributes, which is a lack for traditional multi-view clustering, and in this sense, MCGC is a more general framework compared with other SOTAs. It utilizes graph filtering to smooth node features, graph learning to discard noises and contrastive learning to optimize. And it outperforms other baselines to some degree.

However, here are some drawbacks:
1.	there is a big problem in the organization of the article. In introduction, the authors spend many efforts to describe previous multi-view clustering methods, which is confusing to readers, because the readers probably can not make full sense of these works. Meanwhile, the authors don’t state clearly about their motivation, for example why they should use contrastive learning. It’s difficult for me to follow the story. 
2.	The authors should survey some graph learning models in the part of related work.
3.	For equation 1, I suggest the authors cite another paper “Interpreting and Unifying Graph Neural Networks with An Optimization Framework” [WWW 2021], which is also a related work.
4.	For graph matrix S, it is initialized randomly, so its degree of freedom is very high. And the training of S is an inefficient process.

limitations_and_societal_impact: Yes.
review_title: null

final_score: rating: 7: Good paper, accept
review_content: summary: This paper considers the multiview graph clustering problem where the goal is to find a cluster given multiple networks. The paper integrates the self-expression strategy and contrastive learning mechanism into a unified framework to perform graph learning. Besides, graph filtering is designed to recover a clean data in advance. An optimization algorithm is developed to solve the objective function. The paper compares the effectiveness of their method with other deep methods and demonstrates that the proposed method is competitive and promising.
main_review: Some strong points:
1. Facing the systematic usage of complex deep learning, this paper makes an important contribution by showing that shallow model is still possible to beat deep neural networks-based methods. 
2. The method is simple to implement and easy to understand. The deployment of graph filtering is extremely interesting and could carry a lot of potential for future progress in the field. The combination of contrastive learning and traditional methods also has a lot of potential.
3. Compared to the SOTA methods, the proposed method demonstrates its superiority on five benchmark datasets. Besides, many ablation experiments are carried out to the effectiveness of each component in the proposed objective function.
4. The paper is well organized and easy to follow.
limitations_and_societal_impact: Some concerns and questions:
1. The complexity analysis is missing.
2. Eq.(7) should be explained in more details. For example, it is unclear how to set $\lambda$ in Eq.(7).
3. It would be better to construct some other features and see how the proposed method will work.
4. The paper needs a careful proofreading and some typos should be corrected, e.g. Eq.(11).
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: This paper proposes a multi-view clustering method. They first use graph filter to generate a new node representation for input, and then do self-expression in a linear combination of various views. Afterwards, they propose a graph contrastive regularizer, which mimics the contrastive learning given the nearest neighbors as positive samples and any others as negative ones. Experiments compared with recent works and ablation studies are presented.
main_review: Originality: The task is not new and the novelty seem lies in the contrastive regularizer.

Quality: My major concerns are as follows.
1. The graph contrastive regularizer, the main contribution of this work, is introduced without strong motivation or any deep insights and only takes up less than a half page. This makes the whole paper weak.
2. From Eq. 6, the graph contrastive regularizer uses the neighbors information. That means this task is not a purely unsupervised task unless the neighbors are obtained from the distance of raw features. If so, from my view, this regularizer is close to spectral regularizer. If the authors insist that this new term contributes a lot, then they are supposed to add it on different optimization baselines to see the according improvements.
3. From my understanding, Eq. 6 is similar to the contrastive loss in Raia 2006 if you simply reformulate the objective. Form this view, the improvement happens when the neighbors are reliable.
4. From the alternative optimization, I can see the proposed method only do full batch update, which is not practical for real-world cases due to non-scalability. 
5. The simply used weights are not a good option for missing views or noisy views today. From this aspect, the authors over-claimed the impact of this work.
(1) Raia et al., Dimensionality Reduction by Learning an Invariant Mapping, 2006

Clarity: The submission is clearly written.

Significance: Basically this work is not new for me and is lack of deep understanding. It does improve the current methods from the experimental results while the whole paper is weak.  
limitations_and_societal_impact: I feel this work has some vital limitations like scalability for large dataset. They can use the matrix based method like years ago but the drawbacks of this kind of formulation should be mentioned and discussed.
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: This paper proposes a generic framework for clustering multilayer/multi-view graph data. The main contributions include: 
- A graph filtering technique is developed for high-frequency noise removal;
- A contrastive regularizer is applied to graph learning in a simple way; 
- Solid experiments and state-of-the-art performance evaluated on five graph data.
main_review: Clarity: The paper is easy to follow and well organized.

Reproducibility: Yes, the source code is publicly available.

Strengths：
1. The challenge tackled by this paper seems interesting and the paper has a good motivation. To my best knowledge, the clustering of multi-view graphs is few studied and this is the first attempt to simultaneously handle multilayer graphs with multi-view attributes data.
2. The proposed method is interesting and novel.  Instead of deep neural networks, the authors use graph filtering to obtain a good representation, which is simple and effective. Moreover, the idea of contrastive learning is employed in a new way to learn graphs in this paper. Though simple, the techniques make sense and look novel to me.
3. The experimental evaluation is comprehensive and the results obtained with the proposed method are convincing. In particular, the authors compare many recent methods from different categories.
4. This work could inspire more researchers/practitioners to apply a contrastive regularizer in shallow machine learning methods.

Weaknesses：
1. As shown in Table 1, the authors conduct experiments with multilayer graphs (ACM, DBLP, IMDB) and multi-view attributes (Amazon photos and computers) respectively. Is it possible to evaluate data with multilayer graph and multi-view features?
2. The contributions of this paper should be explicitly highlighted in the Introduction.
3. This paper presents abundant results with five tables. However, the discussion is quite short and should be elaborated in detail.
4. The authors say that the cartesian product is applied to construct the 2nd feature. However, its definition is unclear.
5. The authors should carefully proofread the whole paper.

limitations_and_societal_impact: The authors have discussed the limitation and potential negative impact of the proposed method.
review_title: null

final_score: rating: 5: Marginally below the acceptance threshold
review_content: summary: propose a generic framework to cluster multi-view attributed graph data and borrow the contrastive learning idea to provide a  multi-view contrastive graph clustering (MCGC) method which outpeerforms SOTAs including deep methods.
main_review: Indeed, the submission has good readability and is easy to follow.  proposed MCGC is relatively intuitive and empirically-proved as well effective to some extent,  however, I think its novelty is NOT that significant, using the contrastive learning has had and NOT so unique. In addition, my comments are
1. First there have existed many multi-view clustering (MVC) methods omitted here, secondly, major differences between the traditional MVC and the method for graphs here are unclearly made, especially what challenges will be posed or encountered.
2. On lines 108-109, N-by-d feature matrix X is treated as d "N-dimensional graph signals", obviously, as the N increases, the graph size will be increased accordingly, which seems to bring an impracticality!
3. On lines 115-117, A problem with (3) is if the L is defined by the feature matrix X whose noise will unavoidably propagate into the L, thus H is still noisy unless the L is robustly and jointly learned as well. However, (1) will fail to yield a robust L!
4. On line 143, (7) is just a mixed objective of existing related methods! its optimization is realized via existing techniques.
limitations_and_societal_impact: The authors have pointed out some biases from the data like other methods and remind needing care when deploying the method.
review_title: null

Meta-review: This paper focuses on multiview graph representation learning, proposing a graph clustering model and demonstrating its usefulness against  a number of baselines. The reviewers felt the model was well motivated with clear contributions, though there were some concerns about presentations, namely on how the main contributions are presented. However, the reviewers were satisfied with the promised changes: I encourage the authors to do so as this will make the paper more generally clearer in terms of impact (as well as proofreading). Finally, the reviewers were generally excited to see strong performance of a shallow model, and I agree that this is an important result in the context of the currently dominant "bigger is better" models. Therefore, I recommend acceptance.
Decision: Accept (Poster)