Multi-Scale Diffusion-Guided Graph Learning with Power-Smoothing Random Walk Contrast for Multi-View Clustering
Authors: Feiyang Chen
Ruiqiang Guo
Zhibin Gu
Venue: ICLR 2026 Poster (2026)
Keywords: Multi-view Clustering
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

Abstract: Despite the notable advances in graph-based deep multi-view clustering, existing approaches still suffer from three critical limitations:  (1) relying on static graph structures and being unable to model the global semantic relationships across views; (2) contamination from false negative samples in contrastive learning frameworks; and (3) a fundamental trade-off between cross-view consistency and view-specific discrimination. To address these issues, we introduce \textbf{M}ulti-sc\textbf{A}le diffusio\textbf{N}-guided \textbf{G}raph learning with p\textbf{O}wer-smoothing random walk contrast (\textbf{MANGO}) for multi-view clustering, a unified framework that combines adaptive multi-scale diffusion, random walk-driven contrastive learning, and structure-aware view consistency modeling. Specifically, the multi-scale diffusion mechanism leverages local entropy guidance to dynamically fuse similarity matrices across different diffusion steps, thereby achieving joint modeling of fine-grained local structures and overall global semantics. Additionally, we introduce a random walk-based correction strategy that explores high-probability semantic paths to filter out false negative samples, and applies a $\beta$-power transformation to adaptively reweight contrastive targets, thereby reducing noise propagation. To further reconcile the consistency-specificity dilemma, the view consistency module enforces semantic alignment across views by sharing structural embeddings, ensuring consistent local structures while preserving heterogeneous features. Extensive experiments on 12 benchmark datasets demonstrate the superior performance of MANGO.

Published as a conference paper at ICLR 2026
MULTI-SCALE DIEFUSION-GUIDER GRAPH LEARNING WITH POWER-SMOOTHING RANDOM WALK CONTRAST FOR MULTI-VIED CLUSTERING
Feiyang Chen
Ruiqiang Guo
College of Computer and Cyber Security Hebei Normal University
College of Computer and Cyber Security
Hebei Normal University
laice5 6 9 gmail com
rqguo@hebtuu edu, cn.
Zhibin Gu
College of Computer and Cyber Security Hebei Normal University
guzhibin@hebtu, edu.on en
ABSTRACT
Despite the notable advances in graph-based deep multi-view clustering, existing approaches still suffer from three critical limitations: (1) relying on static graph structures and being unable to model the global semantic relationships across views; (2) contamination from false negative samples in contrastive learning frameworks; and (3) fundamental trade-off between cross-view consistency and view-specific discrimination. To address these issues, we introduce Multi-scAle diffusioN-guider Graph learning with view clustering, a unified framework that combines adaptive multi-scale diffusion, random walk-driven contrastive learning, and structure-awan view consistency modeling. Specifically, the multi-scale diffusion mechanism leverages local entropy guidance to dynamically fuse similarity matrices across different diffusion steps, thereby achieving joint modeling of fine-grained local structures and overall global semantics. Additionally, introduce a random walk- based correction strategy that explores high-probaie probability semantic paths to filter out false negative samples, and applies 3-powwe transformation adaptively reweight contrastive targets, thereby reducing noise propagation. To further reconcile the consistency-speccfccii dilemma, the view consistency module enforces semantic alignment across views by sharing structural embeddings, ensuring consistent local structures while preserving heterogeneous features. Extensive experiments on 12 datasets demonstrate the superior performance of MANGO.
random walk contrast (MANGO) for multi-
INTRODUCTION
Multi-view Clustering (MVC) aims to partition data samples into meaningful clusters by leveraging
the consensus and complementary information across multiple views. By exploiting the synergistic
relationships between diverse data representations, MVC facilitates the discovery of underlying
structures in complex datasets, positioning it as key research area for integrating heterogeneous information sources and revealing intrinsic patterns (Gao et al.) 2015 Liu et al. 2018: 2020 Xu et al. 2022aa Yan et al. 20244.. From the perspective of learning paradigms, existing MVC methods be broadly categorized into traditional techniques and deep learning- based models (Fang et al.) 2023). Among them, deep learning- approaches have attracted increasing attention due to their strong capacity for modeling intricate data distributions and extracting highly expressive feature
representations (Huang et al. 2023 Liu et al. 2024 Tang & Liu 2022
Deep multi-view clustering leverages the nonlinear mapping capabilities of deep neural networks to capture the distinctive semantics of each view while effectively integrating complementary infor-
"Corresponding author

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Published as a conference paper at ICLR 2026
mation, enabling strong performance in complex data scenarios Lin et al. 2023 Xu et al. 2023 Yang et al. 2023). ). Given the ability to explicitly model the topologica relationships within the data, graph- deep mueti-view clustering (GDMVC) methods have garnered considerable attention. For example, Wen et al. 2024) proposed an adaptive hybrid graph filter that combines high- and low-frequency signals with fused multi-view embeddings to improve clustering performance on graphs. Ren et al. (2024) dynamically fuse weighted graphs using deep autoencoders and graph convolution, enabling efficient self-supervised deep multi-view clustering, Additionally, accurately capture the affinity relationships between sample pairs, numerous contrastive learning- driven methods for graph structure refinement have been proposed [Gao et al. (2024); Liu al. (2023); [Smith et al.) (2025). For example, Yu et al. (2025) proposed a multi-view deep subspace clustering method leveraging contrastive learning and Cauchy-Schward divergence for interactive representation and clustering optimization. [Chen et al. (2023) introduced a cross-view contrastive learning model that learns view-invariant and robust representations by contrasting cluster assignments across views. Additionally, Wang et al. (2223) integrated triple contrastive learning at both the feature representation and graph structure layers to generate a consensus similarity graph with a clear clustering structure.
Despite the notable progress achieved by recent graph-based based deep multi-view clustering methods, three fundamental technical challenges remain unresolved. First, the reliance on static graph structures imposes inherent limitations in capturing complex semantics across multiple views. Specifically, such methods only rely on the local neighhorhood relationship between samples to calculate the similarity, ignoring the global semantic connection between views. This limitation leads to inevitable information loss and distortion, which makes it difficult for the model to accurately capture complex cross-view semantic associations. Second, the issue of negative sample contamination remains prominent in graph-based contrastive learning frameworks. When constructing negative pairs, the model may mistakenly treat semantically similar samples as negatives, introducing false contrastive signals. These errors can accumulate through gradient backpropagation, forming positive feedback loop of "noisy optimization" " that progressively degrades the quality of similarity measures and weakens the effectiveness of contrastive learning. Finally, the dilemma of balancing semantic consistency and modality specificity plagues multimodal alignment strategies. This trade-off can undermine the separability of clusters, as aver-alignment harms the uniqueness of the modality, while under-alignment destroys the cross-view semantic correspondence.
To address the aforementioned limitations, this study proposes Multi-scAle reffuusion-uuidee Graph learning with pOwer-smoothing random walk contrast (MANGO) model, which contains three technical innovations. First, we introduce an adaptive multi-scale diffusion mechanism. This module dynamically fuses similarity matrices from multiple diffusion steps based on local entropy information to build a more resilient and semantically expressive graph structure. By modeling on multi-scale topology, MANGO can capture local details between directly connected samples and global semantic connections between distant samples. Second, to address the challenge of negative sample contamination, random walk path sampling is introduced to dynamically correct the sample distribution of contrastive learning. This technique explores high-probabilltyy semantic paths to filter out false negative sample pairs, and is supplemented by B-power transformation to adaptively weight negative samples. The combined method reduces noise propagation and enhances the accuracy of graph similarity estimation through iterative refinement. Third, regarding the trade-off between consistency and specificity, our structure-aware cross-view contrastive learning mechanism achieves dual goal: to enforce semantic consistency through shared structural embeddings, while retaining modality-specific discriminative features through a view-aware attention mechanism. This balance solves the problem of cluster boundary ambiguity by coordinating global semantic alignment and local modality uniqueness. The core contributions of this paper include the following three aspects:
A multi-scale diffuusion mechanism is proposed to break the performance bottleneck brought by the fixed diffusion step size, dynamically fuse the similarity information under different step sizes, take into account both local structure exploration and global semantic modeling, and realize the effective capture of multi quatti-raanulritty structural information.
A random walk correction method is designed to optimize the distribution of contrastive learning targets. The hybrid transfer matrix is constructed by combining the t-step transfer matrix and the unit matrix, and the weight of negative samples is adjusted through 8-power transformation to form a more discriminative contrast target, which reduces the impact of erroneous negative samples.
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We design structure- view consistency module that simultaneously promotes semantic alignment across views and preserves modallty-sppecife discriminative features, thereby improving clustering quality in heterogeneous multi viet scenarios.
Extensive experiments are conducted 12 benchmark multi-view datasets of varying types and scales. The results verify the effectiveness of MANGO compared with several sate--of-theaas art multi-view clustering methods.
2
RELATED WORK
Deep multi-view clustering (DMVC) methods can be broadly categorized into three paradigms based on how they handle inter-view relationships: joint methods, alignment-based methods, and other methods.
Joint methods integrate feature learning and clustering into unified objectives. leveraging cross-view collaboration to enhance representation quality. For instance, Li ct al. (202I) jointly Iearned both view-specific and consensus graphs while adaptively assigning weights to obtain high-confidence clustering results. Xiaa et al. (2022) built self-supervised framework based on Euler transformation and l -norm, integrating representation learning and clustering. Hu et al. (2023) enhanced feature- level alignment by incorporating cluster-level contrastive learning and dynamic weight learning to promote more consistent deep representations.
Aligmment-based methods, in contrast, focus mapping view-specific representations into shared subspace to promote consistency. Early work by Hassani & Khasahmadi (2020) introduced a node- graph dual-grn suul-granullrrty alignment framework to address cross rrosshhirarrhhcal redundancy. Building this, Liu et al.] (2022) mitigated representation collapse by reducing inter-view redundancy from both sample and feature perspectives. [Chen al. (2023) proposed clustering-awart contrastive learning mechanism, which directly enforced semantic consistency across views. [Trosten et al. (2023) identified negative sample bias in traditional contrastive alignment and developed variational alignment model that maximized mutual information.
In addition, some approaches combine these strategies address specific issues such as noise and incomplete views. For example, Luo et al. (2018) pioneered the combination of consistency constraints and view-specific modeling establish unified theoretical framework for multi-view subspace representation. Ke et al. (2021) built full-process integrated framework of feature verifying the feasibility of multi-task joint optimization, Xu al. 2021) introduced a common-specifie variable dual-channel mechanism to separate multi-view shared clustering features from view-unique information.
3 METHOD
This section provides detailed introduction to the proposed MANGO model, which primarily con- sists of four components: the self-expressive module, contrastive learning module, view consistency module, and adaptive diffusion module, as illustrated in Figure
3.1 SELF-EXPRESSIVE MODULE
Given a multi-view dataset {X" E Raxdu pm where m is the number of views, n denotes the number of samples, and d1, d2,..., dm are the dimensionality of each view, we propose a self- expressive module to effectively integrate heterogeneous views. Specifically, for the v-th view, we first obtain its embedded representation through the encoder: Z = f (X"). where f represents the encoder of y-th view, and Z" is the learned embedded representation. The core of the module the encoder that achieves selfsrcconttttuttion through sparse combination of latent features. The reconstruction process defined as X" = CVZ", where CV is the sparse coefficient matrix obtained by weighted fusion of sparse matrices under each view. For the v-th view, the sparse adjacency matrix within the view is generated by filtering the cosine similarity the sample pairs with an adaptive threshold b, ensuring that the reconstruction process captures local structural dependencies.
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Representation Learning
Improve Diserimination
Gr aph Re nement
21
Adaptive Fusion
Bardon Correct
Lin al 21)
Se Exprest
Contrastive Learning
Coms i steney
xm
2
1-
resul is
Affiinity Diffusion
Figure The Framework of MANGO. First, MANGO obtains embedding representations via view-specific MLP modules and captures data structure through reconstruction loss. Then, the view consistency module achieves semantic alignment by sharing structural embeddings, while contrastive learning and random walks filter high-probability semantic paths and eliminate pseudo-negative samples. Finally, the affinity matrix undergoes steps of diffusion, and local structures and global semantics are fused through information entropy-based weighting.
Finally, the reconstruction loss is obtained:
Cree
x
(1)
m=
addition, to prevent overfitting, we use the hybrid regularization term following You et al. 2016):
Lreg = Σ AIC + 1 > |I 2
(2)
v=l
where is used to balance the two regularization terms. This reconstruction loss, combined with hybrid regularization, yiclds embeddings that preserve both global semantics and local geometry, providing robust inputs for downstream modules.
3.2 POWER- DOWER-SMOOTHR SMOOTHING RANDOM WALK ENHANCED CONTRASTIVE LEARNING
Contrastive learning is widely adopted for representation learning in unlabeled multi-view scenarios, where features are aligned by contrasting positive (similar) and negative (dissimilar) pairs. However, relies on two strong assumptions: (1) positive pairs from different views of the same sample are semantically aligned, and (2) all negative pairs are unrelated. These assumptions often fail in practice cross-view heterogeneity can make same-class instances appear dissimilar, producing false negatives (FNs), while the absence of labels makes it difficult to ensure true negatives. Such false negatives distort training signals, disrupt the manifold structure, and weaken the discriminative power of the learned representations.
To tackle this issue, we propose a power-smoothing random walk enhanced contrastive learning strategy, which integrates two key components: random walk correction capture high-order semantic relations, and a power-smoothing operation to reduce the impact of false negatives by refining similarity distributions.
Random walk-based correction mechanism: Traditional contrastive learning assumes equal im- portance for all non-anchor negative samples, overlooking the intrinsic structure of the data. The proposed random walk correction mechanism simulates random walks on the sample manifold to uncover and exploit this structural information, enabling principled weighting of negative
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samples. Specifically, first construct the affinity matrix A., = =xp(-oo [ -z,l²) Euclidean distance of sample embedding, where a is the bandwidth parameter of the Gaussian kernel. After the affinity matrix A is constructed, it needs to be normalized and converted into transfer matrix M, where My represents the one-step transfer probability from sample i to sample j.To better capture high-order manifold structures, compute the t-step transition matrix M -M x M (t times), where M is the one-step transition matrix.
through the
M2 Σk=1 Aa
(3)
Finally, the interpolation parameter is used to balance the self-connection strength and the manifold structure. formulated as T n/I + M where T the target distribution matrix, and T.f denotes the degree to which sample j is a semantic neighbor of sample i. This value can be directly used as the negative sample weight in the intra-view contrastive loss.
wtt---moootiin-innddeee contrastive learning: In addition, in order to enhance the robustness, we introduce a smoothing power operation on the basis of InfoNCE loss to control the overall strength of negative samples, which directly acts on the negative sample term in the contrast loss, thereby obtaining the expression of the intra-view contrast loss:
m
exp
Lintra
log
m
71
exp
<(x".
+
Ejjte Try exp
where is the number of samples, s 2j) represents the cosine similarity, and 3 is the power operation parameter. Compared with the standard InfoNCE loss, the negative sample item is subjected to the 3 power operation. This operation has nonlinear smoothing effect on the negative sample item parameter, which used to reduce the overall impact negative samples, especially the impact of extreme value samples.
Similarly, the expression of the contrast loss between m views is as follows:
exp
Linter m(m 1)
log
(5)
prtti
n
1=1
exp
(=ff x=9)
+ Egft Wij exp
expexp
The difference is that s(z,,, 22} represents the cosine similarity between sample in view and sample j in view q. 2 is the embedding representation of sample i in view p. 2] is the embedding representation of sample
view q, and Wi is the uniform weight.
Finally, by integrating the aforementioned intra-view and inter-view contrastive losses, the Power- Smoothing Random Walk Enhanced Contrastive Learning framework can be formulated as follows, where y is the balance parameter of the two contrast losses.
Lcontra = Lintra + PLinter
3.3 VIEW CONSISTENCY MODULE
Due to potential discrepancies in noise distributions and semantic emphasis across different views. embeddings of the same class can vary significantly among views. Directly inputting such misaligned embeddings into the subsequent fusion module will destroy the intrinsic consistency of the data and fuse the conflicting noise. Therefore, in the last step of representation learning, we introduced a view consistency module. which builds mapping bridge between views to ensure that the representations from different views can be aligned and complement each other. Specifically, view consistency is maximize the mutual information between representations of different views. Given two view embeddings Z and Zz, the mutual information is detined as:
Zz) || (Z', log (ZP) (Z4)
p (Z' Z
1 IZ';
dZ dz
(7)
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I 2 H (h,) Bp(zp,21 (fp-g (Z"), Zd)
(8)
where .) represents cosine distance. The core of the view consistency module is to learn mapping function such that: Z = f (2)) R Z Therefore, the consistency loss function can be defined as Lp-ty = d(Zr,z ) where is used to control the sensitivity of the loss.
For the case of views, we need calculate the consistency loss between all view pairs:
Lconsist m(m
Lp
(9)
3.4 ENTROPY-GUIDER MULTI-SCALE DIFFUSION FOR GRAPH REFINEMENT
After obtaining discriminative representations, a graph A is typically constructed to encode semantic similarities among samples, serving as a foundation for downstream clustering tasks. However, most existing graph-based approaches employ static graph structures, which are highly sensitive to the quality of the learned features. To mitigate this issue, we propose an entropy-gune multi- scale diffusion strategy for graph refinement, consisting of a multi-scale diffusion module and an entropy-guided learning module.
Diffusion-based methods propagate information by repeatedly multi- plying the transition probability matrix, enabling global semantic aggregation. Formally, the 1-step diffusion is A' = A X ... X A, where A is the sparse similarity matrix, A.j denotes the one-step transition probability from node i to 7, and A' represents the transition probability matrix after steps. Unlike traditional diffusion, we compute matrices at multiple diffusion steps capture structural information across scales, and dynamically fuse them to balance local structures and global semantics. Specifically, given the normalized affinity matrix Anorm we construct {A", A!...., A'), where
Multi-scale graph diffusion:
A norm- After each step, we retain only the top-K elements per row and re-normalize to obtain A
Entropy-guided multi-scale graph learning: In this approach, entropy is used evaluate the quality of the diffusion matrix by quantifying the uncertainty or uniformity in the distribution of connection weights. A lower entropy indicates more concentrated distribution, which corresponds more distinct and clearer semantic structures. Specifically, for each row Á in the diffusion matrix Á the entropy computed over non-zero elements, where A denotes the weight of the connection between nodes i and following the t-th diffusion step.
H(A)) = Σ A log A 3:
(10)
Next, the average entropy of the matrix is computed as = H(A)). The inverse of entropy is used as the weight the scale because lower entropy indicates more concentrated clearer category structure. This design enables our diffusion model to automatically adjust the weights and retain complementary information at multiple scales.
distribution, corresponding
T
fusion Σ 1= AAA)
A
(11)
To facilitate subsequent spectral clustering, we further apply symmetric normalization and diagonal enhancement the final diffusion matrix. Specifically, the symmetric normalization is achieved by averaging the matrix with its transpose, while diagonal enhancement performed by scaling the diagonal elements using enhancement coefficient k.
1 fusionl [
A fimatti (i (
+ A A [j.
(12)

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Published as a conference paper at ICLR 2026
3.5 THE OVERALL LOSS FUNCTION
Combining self-representation and regularization losses, by jointly random Walk modified power smoothing contrastive learning and view consistency modules, the overall loss function of proposed MANGO is formulated as follows:
L Creg afree 3Lcontra "Coossist
(13)
where hyper-parameters a, 8, and y balance the importance of the three terms.
EXPERIMENT
4.1
EXPERIMENTAL SETTINGS
Datasets: Twelve datasets with varying types and scales are used: Yale (Chowdhury et al. 2025).
ORL (Du et al. 2022), BBC-Sport (Chowdhury al. 2025), Reuters (Du et al. (022).. Scene-
FFii-Fei & Peronal 2005). MSRC-vI (Du et (002). LandUse-21 (Zong et al. 2022). Caltech 101-
20 (Peng et al., 2019), ALO1-100 (Yuan et al. (025). STL10 Yu al. 2024). HandWritten
(Van Breukelen et al., 1998), and MNIST-3V (Zhou et al. 2020). More detailed descriptions
of these datasets can be found in Table
Baselines: We compare MANGO
with eight SOTA multi-view
Table The detail for experimental datasets
clustering methods, including
MFLYC(2022) (Xu et al. MSESC(2023) Cui al. CVCL(2023) (Chen et al. 2023
2022b).
Dataset
Type
Instances # Classes Views
2023).
Yale
Face
165
3
LSGMC(2023) (Lan et al. 2023
ORL
Face
400
40
3
MVD(2023) (Li et al.] 2023).
DIVIDE(2024) (Lu et al.
2024).
BBC-Sport
Text
544
5
2
SCM(2024) (Luo et al. CANDY(2024) (Guo et al. (024).
2024).
Reuters
Text
1200
Scene-15
Scene
4485
15
3
Evaluation metries: All results are the average of 10 independent runs.
MSRC-v1
Object
210
7
5
LandUse-21
Object
2100
21
We have calculated the average. To comprehensively evaluate clustering performance, we adopt three widely used metries: clustering accuracy (ACC), normalized mutual informa- tion (NMI), and adjusted Rand index (ARI), where higher values indicate better performance.
Caltech101-20
Object
2386
20
6
ALOI-100
Object
10800
100
4
STL10
Object
13000
10
3
HandWritten
Digit
2000
10
6
MNIST-3V
Digit
60000
10
3
For fair comparison, the hyperparameters of all baseline methods carefully tuned based on their publicly available code, and the best best-performing settings are adopted. For the MANGO model, a three-layer MI.P is employed to extract features for each view, with hidden layer sizes 512, 256. The input dimension corresponds to the original feature size, and the output dimension fixed at 256. In all experiments, the bandwidth parameter of the Gaussian kernel is set 0.3, the regularization parameter to 0.3, the interpolation parameter 7) to 1.2, the number of random walk steps é to 3, the temperature parameter to 0.6. The contrastive loss weight μ selected from the set (0.01, 0.1, 1, 10) through a grid search, and is ultimately set to 0.1 across all datasets. Hyperparameters a, B, and & are selected via grid search over the set lee3, (1e4,1e5) and le6). Shallow learning experiments are implemented in MATLAB 2023b workstation with a 2.50GHz 7285H 32-core CPU and 128 GB RAM, while deep learning experiments are conducted using PyTorch 2.5.1 H20-NVLink GPU.
1024,
4.2 COMPARISON RESULTS
Table 2 records the experimental comparison of our proposed MANGO with other comparison methods across twelve datasets, where the best and suboptimal performance are highlighted in bold
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Table 2: Clustering performance of all methods on twelve datasets
Metric
Datuset
MFLYC
MSESC
CYCL
LSGMC
MYD
DIVIDE
SCM
CANDY
MANGO
ACC
0.57219053
0600
605710002
07111906
0600100003
0,90110225
0
0:54720000
0600-0008
067220017
06622000019
056620000
060020029
0.787:28011
ART
03355=0042
0.50000066
0447=0019
0418-00002
0.955:0005
0,502=0015
ACC
0:22:50056
0695
083200029
0.693
0673
06722000119
0.966
KMI
0.573-00031
0.728-00028
0065
0.000
0.95
0.33
10001
0.025-00005
0.000
0:272110030
ACC
0:47:2002
0.992200000
D 0.200:00.00
0.40
0720200054
0669220066
0.959:20012
BBC-Sporr
NMI
0,51420055
0:95=0001
0.33
0.542:0000
0.60
0
055520025
0400+0005
0
0-48:00054
0.584-00051
0.776000022
0:52900000
0622-000000
0.109:00004
051300088
0:58-00072
0,92=80015
ACC
0002
0.00
0.657
200027
0.56
049910001
0,591200024
0.58
Reuters
KMI
0.220.10001
0.020
0.271100051
0.304
0242200000
0.26:100222
0.051100013
00001
0.11-00118
024410005
0.257 20,011
0.291...044
10005
0,52510017
0:201:0011
ACC
0.113:0011
0.323-00225
0.065
0.024
0.250000112
0.365:10022
Sc
KMI
0.327=000K5
0 0.362
0.857
0.40000001
0.381
20,022
0.005
0263000014
0.55900011
0,499
ARI
0.173=00077
0 0.02
0.30
0001
000000
0.301
0150=00009
0,195:0013
0.58:
0567220007
0322
00.007
0.732000015
06652000
040420029
MSRC.v1
KMI
0.72210043
0603
085100004
0.00100.000
0683
059210004
0,287
0
ARI
0/739:10066
000
0833200006
03592200
0.112:0.000
0615200002
049110000
O 0100200054
0.884:2014
ACC
0.2410006
0219
1.0000
00003
0.259
0313
020010010
0222
0.315
LandUse-21
KMI
0.45=00044
0.273-0010
0
0.34-00003
0
00000
0228
0
ARI
0,005=00018
0.104-0.08
0.111
0.157-8000
0.114
0.774
0.27
0.011
0.160=0004
ACC
0663=50011
0472-0009
O 0.50+00.043
0.573
0616
051820.05
0401
0.11=
Callechi01-21
0544200000
0023000000
0627100000
0.72540014
0.59220003
043--001
047220004
0.09
#0.011
004200017
0627:00005
033620019
D.SINE
ACC
0.38110007
0.55
D.M 10.01
0007110015
0.02
0,887 LOMS
ALOI-100
0.775=00055
0.727=0000
000
0.005-000004
00002
0.00000005
0,702
0.9
0.50110011
0.00
1.0000
0516100013
0,50810017
0000
ACC
0.112=0000
0.15
0 0.20200004
0.00
0
095700.015
0.677:0000
0.960
STL10
NMI
0000
0 0.65
0.3820000
20,00000
0828
088210021
0.912200122 061520010
0:00000000
0315=0.079
0600000000
0.77020000
0838+000010
0872+0011
0,99400009
0
ACC
0.85120006
0821046
05781000
027
086710.062
077010005
095120011
0.97810011
HandWriton
KMI
0.549::0.022
0:055
0.65
0.12
0.77
0.697
0.835:20.211
0.948-0001
0.79010009
0.00000000
0822
1.000000000
0,553 20.021
0.12
060010052
0,84200022
0,952
0:00
0.000000000
0.97
OM
0195
0.027
0.994::0.002
0:80-00000
MNIST-3V
KMI
0.994:00019
0.913-0.011
0.7805-10001
OM
OM
0.9541000006
0.862100015
0.964:00003
0.902000011
ARI
0:360:00222
0.946=0.299
0.700420.00
OM
OM
0196==0000
0:330000024
0.901
0,972=00000
and underline respectively, and the abbreviation "OM" indicates the occurrence of out of memory error. Through comprehensive experiments, we have the following observations:
1) Our MANGO model shows excellent clustering performance in all datasets, significantly outper- forming its competitors in some scenarios. In particular, on the ALOI-100 dataset, our MANGO achieves 88. 38.7% ACC, which is about 13.4% higher than the second-best algorithm DIVIDE. These results suggest that MANGO effectively exploits the rich information among multi- view data by jointly leveraging the random walk-enhanced contrastive learning module and the view consistency mechanism.
2) Compared with existing contrastive learning-based algorithms (such as CANDY, DIVIDE, and SCM), the MANGO model achieves the best clustering performance in most cases. This demonstrates that our power- smoothin and random walk-enhanced contrastive learning mechanism effectively improves representation quality, which in turn leads to enhanced clustering results.
3) Shallow methods like LSGMC and MVD perform well on small datasets but struggle with scalability and often face out-of-memory errors on larger ones due to limited representational capacity. Deep methods such as SCM and CANDY improve on this by learning more expressive features, achieving better results on complex datasets like STL and MNIST-3V. Nonetheless, the proposed MANGO model consistently outperforms existing shallow and deep multi-view clustering methods in most cases, demonstrating its superior performance and robustness.
4) To further verify the effectiveness of method, take the HandWritten dataset an example to visualize the clustering results of other MVC methods and our method. The t-SNE results are shown in Figure 2 It can be seen that our method obtains more clear and compact clusters, which further confirms the superiority of our method.
4.3 PARAMETER SENSITIVITY ANALYSTS
This section studies the impact of three syper-parametery a, 3, and y on the MANGO model. Specifically, we perform grid search by adjusting a, B, and y in the set (1e3, 3e3, 5e3, 7e3, 9e3), (1e4,
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(a) MFLVC
(b) MSESC
(c) CVCL
(d) LSGMC
(c) MVD
(1) DIVIDE
(g) SCM
(h) CANDY
ii MANGO
Figure 2: t-SNE visualization of the consensus affinity matrix on the HandWritten dataset
A2C
Figure 3: Parameters sensitivity analysis with parameters a, 8, and y MSRC-v1.
3e4, 5e4, 7e4, 9e4), and [le5, 3e5, 5e5, 7e5, 9e5) respectively. Figure 3 shows how the performance of the model changes with various combinations of these parameters. The results indicate that MANGO model performs well across the specified ranges of a, B, and y, demonstrating robustness to variations these hyperpurameters.
4.4 ABLATION STUDY
Finally, we conduct comprehensive ablation experiments to evaluate the contribution of each module within the MANGO model. Specifically, remove the contrastive loss (Leontrat). view consistency loss (Lconsist). ). false negative (FN) adjustment strategy, and adaptive diffusion module from the complete MANGO model in various combinations and record the corresponding performance. The results on the MSRC-v1 and Reuters datasets are summarized in Table It is evident that the full MANGO model achieves the best performance, demonstrating that the modules work synergistically to deliver superior clustering results. Moreover, the performance of configuration (c) surpasses that of (a), and (h) outperforms (g), indicating that the random walk-enhanced contrastive learning and view consistency modules effectively exploit view consistency, while the adaptive diffusion module efficiently captures the underlying graph structure.
Furthermore, to further verify how multi-scale diffusion jointly models local and global structures, we conducted two experiments. The first was a comparison between the complete MANGO model and its variant (denoted MANGO-w). This variant removed multi-scale fusion and used only fixed 3-step diffusion step size. The experimental results are as follows. The Table show that for large-scale data with complex structures, relying solely structural information at single scale insufficient. The model must be able to simultancously perceive subtle local relationships and macroscopic global communities, which is precisely what multi-scale diffusion provides.

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Table Ablation study on MSRC-vl and Reuters dataset
MSRC-v1
Reuters
Free Lcontra Cconssst random diffusion
ACC
NMI
ARI
ACC
NMI
ARI
(a)
0.770
0.758
0.662
0.502 0.347 0.267
(b)
0.795
0.780
0.699
0.510 0.347 0.269
0.800
0.781
0.700
0.535 0.364 0.286
0.893
0.845
0.902
0.507 0.344 0.258
0.781
0.749
0.660
0.542 0.360 0.279
(
0.790 0.770 0.681
0.546 0.360 0.270
g
0.863
0.818
0.748
0.551
0.340 0.261
(
V
0.950 0.908 0.887
0.587
0.376 0.287
Table 4: Ablation study for multi-scale diffusion
Dataset
Method
ACC
NMI
ARI
Dataset
Method
ACC
NMI
ARI
Yale
MANGO-W
0.715
0.750
0.572 0.582
ORL
MANGO-w
0.928
0.961
0.895
MANGO
0.729
0.757
MANGO
0.926
0.961
0.895
BBC-Sport
MANGO-W
0.953
0.906
0.921
Reuters
MANGO-W 0.499
0.321
0,253
MANGO
0,959
0.907
0,932
MANGO
0.583
0.351
0.281
Scene-15
MANGO-W
0.496
0.502
0.343
MSRC-vI
MANGO-W 0.929
0.875
0.841
MANGO
0.497
0.499
0.388
MANGO
0.949
0.904
0.884
LandUse21
MANGO-W
0.301
0.344
0.148
Caltech101-20
MANGO-w
0.597
0.713
0.494
MANGO
0.315
0.344
0.160
MANGO
0.619
0.725
0.535
ALOI-100
MANGO-W
0.828
0.878
0.731
STL-10
MANGO-W 0.926
0.862
0.837
MANGO
0.887
0.910
0.803
MANGO
0.960
0.911
0.912
HandWritten
MANGO-W
0.973
0.944
0.944
MNIST-3V
MANGO-w
0.985
0.960
0,970
MANGO
0.978
0.948
0.952
MANGO
0.987
0.962
0.972
CONCLUSION
This paper proposes novel deep multi-view clustering framework. which effectively learns multi- view embedding representation with strong discriminative power by integrating the random walk modified contrastive learning module and the view consistency module. Among them, the random walk modified contrastive learning module enhances the adaptability of the model to complex data distribution by dynamically adjusting the weights of negative samples; the view consistency module realizes deep alignment across view feature spaces through bidirectional projection mechanism. In addition, the introduction the adaptive diffusion module can dynamically capture the multi-scale structural information of the data, effectively avoiding the over-smoothing and information loss problems commonly seen in traditional methods. Extensive experiments fully verify the superiority and effectiveness of MANGO.
ACKNOWLEDGEMENTS
This work supported in part by the National Natural Science Foundation of China (No. 625061 16), in part by the Hebei Natural Science Foundation (No. F2025205006). and in part by the Science Research Project of the Hebei Education Department (No. BJ2026004).
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ETHICS STATEMENT
In this study, propose novel deep multi-view clustering framework to enhance its representation learning capabilities. This research did involve human subjects, human-related data (e.g., personal identifiers, behavioral records), or animal subjects. This research did not receive any external sponsorship or funding, and none of the authors have any financial, professional, or personal conflicts of interest. Throughout this research, experimental procedures, data analysis, and result interpretation were performed an objective and transparent manner, with full records maintained for verification. No ethical violations, such as data fabrication, manipulation, or plagiarism, occurred at any stage.
strictly adhered to the principles of research integrity. All
REPRODUCIBILITY STATEMENT
To ensure the reproducibility of our work, we have uploaded the source code. All datasets used in our experiments are from public datasets. In addition, all experimental procedures and result reports follow transparent standards. Section 4.1 of the main paper details the evaluation metrics (e.g.. NMI, ACC, ARI) and includes the hyperparameter search range. To account for the randomness of model initialization and data partitioning, we report the average results of 10 independent runs.
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A TECHNICAL APPENDICES AND SUPPLEMENTARY MATERIAL
A.1 ALGORITHM
The entire algorithm of MANGO is summarized in Algorithm
Algorithm The algorithm of MANGO
1: Input: Multi-view data {X" Rxx a Je=1; Training iterations E; Trade-off coefficients a; B
and y; diffusion steps T.
2: Output A final-
3: for epoch = 1 E do
4: Compute embeddings {Z" via {MLP")
5: Compute reconstruction loss through Eq.
6:
Compute regularization lossCreg through Eq.
7:
Compute contrastive lossLinter through Eq.
5
8: Compute consistency lossCconeist through Eq.
9: Update the network by optimizing C in Eq. 13
10: end for
Build affinity matrix A.
12: for === 1 to 7' do
13:
Compute the information entropy of A through Eq.
10
14: end for
15: Compute final affinity m final through Eq.
12
16: Performing the spectral clustering on A final obtain the final clustering results.
A.2 ADDITIONAL. EXPERIMENTAL. RESULTS
In this section, we provide complete results on all datasets, including parameter sensitivity analysis and ablation experiments. Table 5 Table T0 summarize the ablation studies of MANGO on all datasets for the three loss items and other improvements, while Figure shows the sensitivity of MANGO to parameters B, and 7 all datasets.
As shown in Table to Table 10 the complete MANGO model consistently outperforms its ablated variants across all datasets. This demonstrates that the integration of the self-expressive module, contrastive learning module, view consistency module, and adaptive diffusion module enables MANGO to fully exploit the rich information embedded in multi-view data, thereby enhancing clustering performance.
As for the parameter sensitivity analysis, Figure 4 demonstrates that MANGO consistently achieves stable and accurate clustering results across all 12 datasets over broad range of parameter values, highlighting its robustness and practical reliability.
Table 5: Ablation study on Yale and ORL dataset
Yale
ORL
Lrec Leontra Leonsist random diffusion
ACC
NMI
ARI
ACC
NMI
ARI
0.260
0.306 0.058
0.923
0.963 0.892
6
0.670
0.708 0.517
0.930
0.960 0.893
0.684 0.704 0.514
0.925
0.961 0.895
0.712 0.743 0.568
0.930
0.959 0.885
(
0.667 0.717 0.533
0.925 0.964 0.896
1
0.694 0.744 0.567 0.933 0.964 0.904
0.687 0.716 0.526
0.931 0.964 0.897
0.716 0.751 0.584
0.943
0.966 0.911
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Table Ablation study on BBC-Sport and Scene-15 dataset
BBC-Sport
Scene-15
Cree Lcontra Lconsist random diffusion
ACC
NMI
ARI
ACC
NMI
ARI
0.778 0.786 0.682 0.481
0.493 0.312
0.695 0.776 0.648
0.492
0.495 0.331
0.701 0.764 0.590 0.493
0.498 0.327
0.959 0.871 0.912
0.493
0.494 0.333
6
0.432 0.200 0.107
0.477 0.493 0.321
0.416 0.179 0.099 0.493
0.503 0.330
0.681 0.712 0.558 0.490
0.496 0.330
0.965 0.885 0.912 0.498
0.504 0.339
Table 7: Ablation study on MSRC-vI and Reuters dataset
MSRC-vI
Reuters
Dree Leantra Lemsiitt random diffusion
ACC
NMI
ART
ACC
NMI
ART
(a)
0.770
0.758
0.662
0.502
0.347
0.267
0.795
0.780
0.699
0.510
0.347 0.269
(c)
0,800
0.781
0.700
0.535
0.364
0,286
(d)
V
0,893
0.845
0,902
0.507
0.344
0,258
(e)
V
0.781
0.749
0,660
0.542
0.360 0.279
V
0.790
0.770
0.681
0.546
0.360 0.270
(g)
/
0.863
0.818
0.748
0.551
0.340 0,261
(h)
V
0.950
0.908
0.887
0.587
0.376 0.287
Table 8: Ablation study on LandUse-21 and Caltech101-20 dataset
LandUse-22 -1
Caltech101
101-20
Cree Lcontro Lconsist random diffusion
ACC
NMI
ARI
ACC
NMI
ARI
0.287 0.329 0.166 0.586
0.719 0.494
0.302 0.352 0.151
0.607 0.713 0.527
0.317 0.349 0.160
0.632 0.738 0.544
0.289 0.330 0.136 0.639 0.724 0.552
0.324 0.345 0.160 0.552 0.687 0.460
1
0.316 0.361 0.157 0.587 0.715 0.477
0.292 0.336 0.139
0.628
0.722 0.552
0.327 0.352 0.163 0.641
0.733 0.554
Table Ablation study on ALOI-100 and STL10 dataset
AL.0I-100
STL10
Lree Lcontra Loonsist random diffusion
ACC
NMI
ART
ACC
NMI
ARI
0.827
0.893 0.745 0.901
0.832 0.776
0.856
0.894 0.759
0.550
0.578 0.391
0.872 0.906 0.789
0.841
0.815 0.759
0.868 0.902 0.776
0.866
0.820 0.708
0.868 0.901 0.774 0.702 0.753 0.548
0.869 0.900 0.776 0.618 0.582 0.417
0.871 0.901 0.797 0.781
0.795 0.666
0.891 0.911 0.805
0.968
0.920 0.930
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Table 10: Ablation study HandWritten and MNIST-3V dataset
HandWritten
MNIST-3V
Cree Lcontra Lconeist random diffusion
ACC
NMI
ARI
ACC
NMI
ARI
@
0.967 0.926 0.926 0.949 0.928 0.898
0,971 0.935 0.935 0.988 0.966 0.974
0.974 0.940 0.942
0.986
0.961
0.970
0.977 0.946 0.948 0.988
0.964 0.973
6
0.970 0.933 0.933 0.956 0.932 0.909
0.977 0.944 0.948 0.988
0.964 0.973
0.970 0.934 0.934 0.988
0.965 0.973
v
0.978 0.948 0.951
0.989
0.966 0.975
(a) Yale
(b) ORL
(c) BBCC-Sport
(d) Reuters
(c) Scene-15 15
(f) MSRC-v1
LandUse-22 21
(h) Caltech101-20
(i) ALOI-100
(i STL10
(k) HandWritten
(I) MNIST-3V
Figure 4: Sensitivity Analysis of the MANGO Model Parameters a, 9 and y Twelve Datasets
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A.3 THE USE OF LARGE LANGUAGE MODELS
During this research and the writing of this paper, we incorporated a Large Language Model (LLM) auxiliary tool improve text processing efficiency and facilitate preliminary literature search preparation. It should be clarified that this tool's use was strictly limited to auxiliary tasks and did not participate in the core aspects of this research, including but limited research design, primary data collection, experimental workflow, statistical analysis, and the derivation and demonstration of scientific conclusions. The scientific integrity, rigor, and originality of the core research content are the sole responsibility of the authors.
Specifically, the LLM's 's auxiliary role in this research focused on the following two aspects:
(1)Text Polishing and Grammar Standardization: Optimizing the language expression of selected paragraphs in the first draft of the paper primarily involved correcting grammatical errors, improving sentence fluency, and assisting with standardizing academic terminology to ensure that the text adheres to the language logic and formatting requirements of academic writing. The final text's academic content, logical structure, and core ideas were all reviewed and confirmed by the authors.
(2)rreimmnnrry Literature Review Assistance: During the literature search phase, LLM assisted in generating preliminary conceptual framework and keyword list for specific research field, providing reference for the authors to determine the scope of their literature search and select their search strategy. It should be emphasized that all the literature included in the literature review of this study were read in full by the authors one by one, and their research relevance, content accuracy and academic value were independently verified before final determination. The literature recommendation results generated by the model were not directly used.
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Reviews:
[{"review_id": "MJVIRrvYWV", "reviewer": "Reviewer_tTks", "summary": "This paper proposes a novel deep multi-view clustering framework named MANGO, which learns a discriminative multi-view embedding by integrating three key components: a random walk–modified contrastive learning module, a view consistency module, and an adaptive diffusion module. Specifically, the contrastive module dynamically adjusts the weights of negative samples to better handle complex data distributions; the view consistency module achieves deep alignment across feature spaces via a bidirectional projection mechanism; and the adaptive diffusion module captures multi-scale structural information while mitigating over-smoothing and information loss. Extensive experiments demonstrate the effectiveness of the proposed MANGO.", "questions": "See Weakness section.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.The paper presents a well-structured deep multi-view clustering framework that integrates random walk–modified contrastive learning, view consistency, and adaptive diffusion modules, each complementing the others effectively.\n2.Extensive experiments conducted on 12 multi-view datasets of varying scales and types convincingly demonstrate the superiority and robustness of the proposed method.\n3.The implementation details of the experiments are presented in a detailed manner.", "weaknesses": "1. The manuscript does not clearly explain the physical meaning of the transfer matrix $M$ in the random walk-based correction mechanism. Its connection with the similarity matrix and its role in the correction process require further clarification.\n2. The rationale behind the hybrid regularization in Equation (2) is insufficiently detailed. The authors should clarify how this regularization helps prevent overfitting and improves model generalization.\n3. The manuscript does not indicate the absence of standard deviations in Table 2, nor does it clarify whether the reported results are based on a single run or the average performance over multiple runs.\n4. The indexing of equations in the pseudocode is not consistent or properly formatted.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "hjqKuTeTLq", "reviewer": "Reviewer_FL2j", "summary": "This paper proposes MANGO, a deep multi-view clustering framework that integrates random walk–based contrastive learning, view consistency alignment, and adaptive diffusion to learn discriminative and robust multi-view embeddings. The authors validate the effectiveness of the proposed method through extensive experiments, including performance comparisons, ablation studies, and sensitivity analyses.", "questions": "See Weakness.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "The framework effectively combines improved contrastive learning, view consistency, and adaptive diffusion in a unified architecture, providing a coherent approach to multi-view clustering, and the experimental evaluation across datasets of varying types and scales provides strong evidence for the effectiveness of the proposed method.", "weaknesses": "1. The variables in the flowchart are not clearly labeled; many matrices and components lack explanation.\n2. Formula (6) involves a balancing parameter, but the strategy for choosing its value is not described.\n3. The manuscript claims that the multi-scale diffusion mechanism jointly models fine-grained local structures and overall global semantics; however, it is unclear whether any evidence is provided to support this claim. Further clarification or validation is needed.\n4. The description of the consistency loss (Formula 9) is unclear, making it difficult to understand.\n5. In the related work section, deep multi-view clustering methods are categorized into joint methods, alignment-based methods, and other methods, but the basis for this classification is not explained.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "HGhUKgawnB", "reviewer": "Reviewer_oBPZ", "summary": "The manuscript introduces a MVC framework built on graph structures, aiming to address limitations in existing approaches. The framework combines three core modules: a multi-scale diffusion process guided by local entropy to integrate similarity matrices and capture both local and global semantics; a random walk–based correction mechanism that reduces the impact of false negatives via a $\\beta$-power reweighting scheme; and a structure-aware view consistency module that aligns embeddings across views while preserving view-specific features. Experiments on 12 benchmark datasets indicate that the proposed method outperforms all baseline approaches.", "questions": "See weaknesses.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. The proposed method employs an interesting multi-scale diffusion mechanism to overcome the performance bottleneck caused by fixed diffusion step sizes. It dynamically fuses similarity information across different steps while considering both local structure exploration and global semantic modeling, enabling effective capture of multi-granularity structural information.\n2. The experimental section includes not only quantitative performance comparisons but also visualizations of graphs produced by different methods, offering deeper insights.\n3. The code is provided by the authors in the supplementary materials, enabling reproducibility.", "weaknesses": "1. It is unclear how the view-aware attention mechanism mentioned in the Introduction, as part of the structure-aware cross-view contrastive learning mechanism, is implemented in the Method section. The authors should clarify this.\n2. The uniform weight matrix W appearing in Equation (5) lacks a clear definition. The authors should explicitly describe how this matrix is formulated or obtained.\n3. The graph refinement process involves T-step diffusion, but the effect of different T values on model performance is not discussed. It is also unclear whether the same T value is applied across different datasets.\n4. Several formatting issues are noted in the manuscript. In particular, some references lack page number information.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "cAZg1kW3cg", "reviewer": "Reviewer_1JZN", "summary": "This work tackles several challenges in graph-based deep multi-view clustering, such as limited representation from static graphs, the influence of false negatives in contrastive learning, and the balance between cross-view consistency and view-specific discrimination. To address these, the authors introduce MANGO, a unified framework that combines adaptive multi-scale diffusion, random walk-based contrastive learning, and structure-aware view consistency modeling, and validate its effectiveness on multiple datasets.", "questions": "On the Reuters dataset, the proposed method outperforms other baselines in terms of ACC and NMI, but performs significantly worse than the CVCL model in ARI. Could the authors clarify the reason for this discrepancy?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.\tThe model is well-motivated and effectively leverages rich information from multi-view data by combining adaptive multi-scale diffusion, random walk-based contrastive learning, and structure-aware view consistency modeling, leading to improved clustering performance.\n2.\tThe workflow is clearly illustrated, accompanied by precise explanations.\n3.\tThe baselines used for comparison are representative, covering both deep learning-based and shallow methods.", "weaknesses": "1. The power operation in the power-smoothing-induced contrastive learning is not described in detail, and the underlying mechanism that gives it an advantage over the standard InfoNCE loss is unclear.\n2. The relationships between different modules are not well explained and feel somewhat disjointed, for example, the connection and distinction between component A in the Graph Refinement module and the random walk-based correction mechanism.\n3. The experimental section does not provide the sources of the datasets used.\n4. The analysis of Figure 2 (t-SNE visualization) in the paper is rather superficial and lacks in-depth discussion.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)