Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper focuses on multi-view graph clustering. The author studied a practical problem in multi-view graph clustering, i.e., anchor-unaligned problem. To solve this, the author propose a new anchor graph fusion framework including an anchor alignment module to solve AUP. Experiments on several datasets show reasonable performance improvement and effectiveness of the proposed method.
strengths_and_weaknesses: Strengths:
1. The motivation is strong and practical.
2. The solution to obtain the permutation matrix is interesting, which is proved effective theoretically and experimentally.
3. This paper is the first work to study the AUP in multi-view graph clustering.

Weaknesses:
1. The reviewer suggests improving Fig.2 for better clarity. Current figures cannot deliver the main idea of the proposed method.
2. The authors do not clearly define the experimental settings in the real-world multi-view datasets and how they tune the parameters. Do these datasets still suffer from the AUP problem? 
3. The visualizations of the anchor graphs on UCI digits show little difference between the aligned and unaligned cases, which is less convincing.
questions: 1. What is the difference between Eq.8 and Eq.9？Detailed proof is expected.
2. In training, the permutation matrix is computed by solving Eq.9, which is actually relaxed. How to get the permutation matrix (binary value) in the inference period?
limitations: N/A
review_title: null

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: This paper focuses on the large-scale multi-view anchor graph fusion strategies where the anchor graphs in individual views are not naturally aligned. The authors discover this phenomenon and provide a matching framework to address this issue. The paper is well-written and easy to read. The idea is well-motivated with clear contributions of illustrating necessity of multi-view anchor alignment. The comprehensive experimental results not only show performance improvements with the proposed FMVACC but also enhances existing large-scale baselines with more flexibility, indicating the effectiveness of multi-view anchor alignment.
strengths_and_weaknesses: Strength:
1.	The paper is well-written and easy to read. The idea of recognizing the multi-view anchor alignment problem has been overlooked in the existing literature which it can benefit further research and community.
2.	The authors propose the first study of generalized flexible multi-view anchor graph framework. Different from sampling fixed anchors for AUP problem in SOTA, a matching framework has been made with more flexibility and performance improvement. 
3.	The effectiveness of the alignment module has been proved both on simulated and real-world datasets. Moreover, existing baselines enjoy considerable performance improvement with the module.
4.	The method can be applied to large-scale scenarios where the scalability is proved by theory analysis and experiments.
5.	Experiments compared with recent works and ablation studies are well presented. 

Weakness:
1.	Although the proposed method focuses on multi-view graph method, the literature review can be enlarged with other strategies of multi-view clustering approaches, for example, multi-view NMF and multi-view ensemble clustering.
2.	The authors should illustrate how to initialize single-view anchors since initializations are important.
3.	There are some formatting inconsistencies in the text that the authors should check them carefully, i.e. AUP.


questions: 1.Some details should be clarified in Table 2 with representative large-scale multi-view methods. RMKM is not a graph method and should be introduced in the related work sections.
2.The authors should illustrate how to initialize single-view anchors since initializations are important.
3.There are some formatting inconsistencies in the text that the authors should check carefully, i.e. AUP and \mathcal for complexity analysis.
4.Some other strategies of multi-view clustering should be discussed in the introduction part, for example, multi-view NMF and multi-view ensemble clustering.
5.I suggest the authors can compare the performance of some latest multi-view scalable graph clustering methods [1][2]. [1] is an extension version of LMVSC mentioned in the manuscript and [2] utilizes NMF into multi-view anchor graph study.
[1]Structured Graph Learning for Scalable Subspace Clustering: From Single View to Multiview. IEEE TCYB, 2021.
[2]Fast Multi-View Clustering via Nonnegative and Orthogonal Factorization. IEEE TIP, 2021.

limitations: Yes. The authors have adequately addressed the limitations and potential negative societal impact of their work.
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper studies an important problem, i.e., anchor-based large-scale multi-view clustering-Anchor Unaligned problem (AUC). The authors review existing multi-view anchor fusion strategies and propose to establish alignment between multi-view anchor sets. The results are promising, and the effectiveness of anchor alignment has been proved.
strengths_and_weaknesses: Strength:
1.	The idea is interesting and novel. Figure 1 and Table 1 illustrate the issue of anchor-unaligned problem for existing multi-view anchor graph clustering.
2.	This paper introduces a flexible anchor graph fusion framework termed FMVACC to tackle the AUP problem, which is a generalized large-scale multi-view anchor graph study.
3.	The authors solve the unmeasurable multi-dimensional anchor matching problem by introducing two parts: feature and structure correspondences in Algorithm 2.
4.	The experimental results seem promising. The effectiveness of the proposed anchor-aligned module has been proved in Table 1/3 and Figure 3/4.
5.	The code is available and easy to reproduce.

Weakness:
1.	The complexity analysis is missing and I suggest the authors should also list with existing baselines (i.e, SFMC and LMVSC) both space and time complexity.
2.	The paper needs careful proofreading and some typos should be corrected.
3.	For better representation, Figure (e)(j)(o) in Figure 3 are the obtained permutation matrices and more discussions can be introduced.

questions: 1. Although the settings are a little different, the authors could discuss the differences between PVC and the proposed method in details. How can they apply to PVC settings?
2. The paper needs careful proofreading and some typos should be corrected, e.g. Eq.(3). Definitions for the constraints should be presented.
3.The complexity analysis is missing and I suggest the authors should also list with existing baselines (i.e, SFMC and LMVSC) both space and time complexity.
4. Figure (e)(j)(o) in Figure 3 are the obtained permutation matrices and more discussions can be introduced.
5. The authors should survey not only multi-graph clustering models but also with other multi-view approaches in the part of related work.

limitations: YES
review_title: null

Meta-review: metareview: All reviewer agree that this paper is innovative and well-written, so I recommend to accept.
confidence: Certain
Decision: Accept