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REVIEW 5 major objections 6 minor 68 references

Scalable Attribute-Missing Graph Clustering via Neighborhood Differentiation

T0 review · 5 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read On large attribute-missing graphs, storing exact-hop neighborhood shells as non-overlapping views lets existing clustering methods perform best or near-best at a 60% missing rate.

desk verdict Useful and believable preprocessing result, but the paper's completeness, memory, and no-propagation claims overreach; worth a serious referee with revisions. read the letter →

arxiv 2507.13368 v2 pith:XWXVUVX5 submitted 2025-07-09 cs.SI cs.AI

Yaowen Hu , Wenxuan Tu , Yue Liu , Xinhang Wan , Junyi Yan , Taichun Zhou , Xinwang Liu This is my paper
classification cs.SIcs.AI
keywords deepgraphclusteringattribute-missinggraphsmulti-viewneighborhooddifferentiationfeaturepropagationlarge-scalepreprocessingunsupervisedlearning
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Large real-world graphs often arrive with many node attributes missing, and deep graph clustering methods either assume complete features or run out of memory when the graph is too big. This paper claims that the real problem is not the missing numbers but how graph structure is used: message passing rereads the same neighbors at every layer, and sampling for scalability destroys topology. CMV-ND is a preprocessing recipe that replaces both with K+1 complementary views: the node's own features plus the averaged features of nodes at exactly one, two, and up to K hops away, with shells that never overlap. Any existing clustering method can take these views as input, and the paper shows that combining CMV-ND with Dink-Net yields the best or near-best accuracy on six standard graphs at a 60% missing rate, including graphs where the previous attribute-missing specialist cannot run. Because the views are built once and stored, the approach scales to millions of nodes without sampling or propagation.

What carries the argument

The load-bearing object is the k-differential hop neighborhood, D^k(v)=N^k(v)\$N^{{k-1}}$(v), the set of nodes whose shortest-path distance from v is exactly k. The method computes these shells by recursive neighborhood search, a breadth-first expansion bounded by K, and then forms view k as the mean feature vector of the nodes in D^k(v); view 0 is the target node's own feature vector. The construction isolates each layer of structure so that no node appears in more than one view, which is the mechanism behind the paper's redundancy argument and the claim that the K+1 views are complete but non-redundant.

What would settle it

Take a large graph whose edges deliberately violate homophily, or construct one by rewiring Cora or Amazon-Photo to connect nodes from different classes, mask 60% of features, run feature propagation, and compare Dink-Net with and without CMV-ND; the paper's claim predicts CMV-ND still improves accuracy, whereas the homophily-dependent consistency argument would predict the gain shrinks or reverses.

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Extended reading notes

Core claim

The paper's central claim is that a graph's structural information can be preserved completely and without redundancy by partitioning each node's neighborhood into exact-hop shells. For each node v it defines the k-differential hop neighborhood D^k(v)=N^k(v)\$N^{{k-1}}$(v), the nodes at distance exactly k, and represents that shell by the mean feature vector of its members. Stacking these K shell means with the node's own feature vector gives K+1 views. Since recursive neighborhood search finds each node once and the differential rule removes overlaps, the views are complete and non-redundant, and any downstream deep graph clustering or multi-view clustering method can consume them. The empirical assertion is that this preprocessing significantly improves clustering on attribute-missing graphs: paired with Dink-Net, CMV-ND achieves the best or near-best ACC, NMI, ARI, and F1 across Cora, CiteSeer, Amazon-Photo, Reddit, ogbn-arXiv, and ogbn-products at a 60% missing rate, without hurting any tested method on any dataset.

Load-bearing premise

The approach assumes the features that fill the missing attributes, delivered by feature propagation before any clustering runs, are accurate enough that averaging them within a hop shell still encodes cluster identity; if imputation is noisy or the graph is not homophilous, the shell averages may add blur instead of structure.

Editorial extensions

If this is right

  • Any existing deep graph clustering method can be upgraded by CMV-ND preprocessing alone, without retraining or modifying its loss, and in the paper's experiments the upgrade never reduces performance on any dataset.
  • At a 60% missing rate, CMV-ND plus Dink-Net completes runs on Reddit, ogbn-arXiv, and ogbn-products, where the previous state-of-the-art attribute-missing method exhausts a 24 GB GPU.
  • Because the views are built once and stored, the preprocessing cost is one-time and CPU-only, with memory linear in the number of nodes.
  • The same K+1 views can be fed to multi-view clustering algorithms, so the paradigm turns a graph-clustering problem into a multi-view clustering problem.
  • Simply concatenating the views and running K-means outperforms most existing deep graph clustering baselines, indicating that the structure stored in the shells carries most of the clustering signal.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper, if the benefit comes from explicit structure rather than propagation, the same preprocessing should carry over to other unsupervised node tasks, such as community detection or unsupervised node ranking, on attribute-missing graphs; the paper does not test these.
  • On non-homophilous graphs the mean-over-shell view may smear clusters, so a useful stress test is to apply CMV-ND to datasets whose edges connect unlike classes and measure whether the Dink-Net gain disappears.
  • The frozen mean pooling over each shell is a simple choice; weighting nodes inside a shell or learning a per-hop fusion could plausibly improve on the reported numbers, since the paper only concatenates or directly feeds the views.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 6 minor

Summary. The paper proposes CMV-ND, a preprocessing paradigm for deep graph clustering on large attribute-missing graphs. For each node, CMV-ND performs a recursive neighborhood search to identify exact-hop shells (differential neighborhoods), mean-pools node features within each shell, and stacks these K+1 views together with the node's original features. The views can either be concatenated into a fused feature vector for a downstream DGC method or fed directly to multi-view clustering (MVC) methods. Experiments on six datasets at a 60% missing rate, after FP imputation, report consistent ACC/NMI/ARI/F1 improvements over DGC baselines and show that K-means on the fused features outperforms most DGC baselines. Appendix D.3 provides a control experiment with propagation-based views, and Appendix D.1 reports gains on three additional attribute-missing baselines.

Significance. If the reported gains hold, the core idea is simple and practically useful: it converts graph structure into view features, allowing methods that either ignore topology or cannot scale to large graphs to benefit from structural signals under attribute missingness. The paper also bridges DGC and MVC, and the plug-and-play design means the contribution could be adopted by many existing pipelines. Credit is due for the breadth of the empirical evaluation (six datasets, four metrics, ten runs), the appendices with additional baselines and propagation-view controls, and the fact that the only hand-set hyperparameter K is fixed after sensitivity analysis rather than tuned per dataset. The significance is tempered, however, by ambiguity about the experimental protocol, apparent inconsistencies in the complexity analysis, and a few suspicious duplicated numbers that call for verification.

major comments (5)
  1. [Appendix B / Section 4.2] The paper never states whether downstream DGC methods receive the original adjacency matrix alongside the fused CMV-ND features. Appendix B only says that h_fuse_v (Eq. 9) can be directly used as input for DGC. This ambiguity makes the central no-propagation claim and the Table 1 comparisons under-specified: if the original adjacency A is also passed, the overall pipeline still uses GNN propagation and the 'without propagation' claim is false; if A is withheld, the with-CMV-ND rows are not the same DGC methods as the without-CMV-ND rows, confounding the comparison by the availability of graph topology. Please specify the exact inputs to each downstream method and re-frame the claims accordingly.
  2. [Section 3.3.3, Eq. (3)] The abstract's claim that CMV-ND preserves structural information 'in a complete but non-redundant manner' is too strong for the actual construction. Eq. (3) collapses every node in D^k(v) to a single mean vector, discarding intra-shell structure such as node identities, degrees, and pairwise connections. What is complete is the coverage of hop shells, not the structural information itself. Please either define 'completeness' formally as shell coverage or soften the claim to avoid overstating what the view representation encodes.
  3. [Section 3.4.2 / Table 3] The memory complexity claim of O(n) is inconsistent with the actual output of CMV-ND. Equation (5) stores a tensor H of size (K+1) x N x d, which is Θ(KNd) memory. For Reddit (N=232,965, d=602, K=7), this is approximately 4.5 GB in float32, yet Table 3 reports a CPU memory cost of only 446.64 MB. Please clarify what is being measured (e.g., peak incremental memory vs. total storage), correct the asymptotic memory analysis, and reconcile the reported numbers with the tensor dimensions.
  4. [Section 3.4.1 / Algorithm 1 / Table 3] The time complexity analysis O(nΔ^k) understates the practical cost of per-node BFS on the large datasets. In a graph like Reddit or ogbn-products, a 7-hop BFS from a high-degree node can cover a large fraction of the graph, making the per-source cost Θ(N+E) and the total cost superlinear/quadratic in N. The reported preprocessing times (189 s for Reddit, 274 s for ogbn-products) are difficult to reconcile with an interpreted per-node BFS implementation. Please describe the actual implementation (e.g., global visited sets, early stopping, C++/numba acceleration) and report scaling behavior with N or with K.
  5. [Table 1 / Table 2] The CiteSeer MVGRL row in Table 1 reports exactly identical values with and without CMV-ND (ACC 57.20, NMI 34.12, ARI 27.62, F1 53.85), and the same four values also appear for DIMVC in Table 2. Since CMV-ND changes the input features, identical results across different inputs cannot arise by chance and indicate a reporting error. Please verify these entries and correct any transcription mistakes, as these values directly affect the claim that CMV-ND never hurts performance.
minor comments (6)
  1. [Section 3.3.3, Eq. (3)] Equation (3) divides by n_v^k, which is undefined when a node has no k-hop neighbors (e.g., disconnected components or k=0). The pseudocode in Appendix F uses a zero vector for empty shells; this should be stated explicitly in the main text.
  2. [Section 3.4 / Table 4] The notation Δ is defined as the maximum degree in Table 4 but as the average degree in Section 3.4. Please use a consistent definition, since the complexity expression O(nΔ^k) is sensitive to this distinction.
  3. [Section 4.3 / Figure 3] The hyperparameter analysis in Section 4.3 references Figure 3, but the figure is not included in the submitted text. Please include the plot with labeled axes and a clear description of which metric (ACC or F1) is shown.
  4. [Table 7 caption] The caption of Table 7 says 'attribute-missing graphs,' but the table reports experiments on attribute-complete graphs (Q4). Please correct the caption.
  5. [References] The baseline 'FP' is cited as (Park et al., 2022), but the listed Park et al. 2022 reference appears to be the CGC contrastive clustering paper, not a feature-propagation method. Please verify and correct the citation for the feature propagation baseline.
  6. [Section 2 heading] The heading 'Relate Work' should be 'Related Work'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: CMV-ND's construction is definitional and its gains are measured on external datasets against published baselines.

full rationale

The derivation chain of CMV-ND is self-contained. The recursive neighborhood search (Eq. 1) and differential neighborhood D^k(v)=N^k(v)\N^{k-1}(v) (Eq. 2) are set-theoretic constructions; the multi-view features h^k_v (Eq. 3) are deterministic mean aggregations over those sets. No clustering output, test label, or downstream loss is used to define these views, so there is no fitted-input-called-prediction pattern. The only tuned quantity is K, set globally to 7 after a sensitivity analysis that shows insensitivity across datasets; this is a hyperparameter choice rather than a per-dataset fit to the reported metrics. The redundancy-ratio analysis (Eqs. 6-8) is a counting argument true by construction, but it is decorative and does not enter the experimental pipeline. The paper does use Dink-Net and AMGC, which have overlapping authors, as baselines, but their performance is measured in this paper rather than imported by citation; the improvement claim is an empirical comparison, so the self-citations are not load-bearing. Two non-circular limitations should be noted: Appendix B (Eq. 9) does not specify whether downstream DGC methods also receive the original adjacency A, making the 'no propagation' claim under-specified; and Section 4 states 'We do not conduct an ablation study' because the components are 'inherently indivisible,' which is a missing-evidence statement. These affect reproducibility, not circularity. No equation or fitted parameter reduces to the target result by construction.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The central method introduces one free hyperparameter (K) and depends on standard graph-search primitives plus two domain assumptions: homophily and informativeness of FP-imputed features. No new physical or theoretical entities are introduced.

free parameters (1)
  • K, number of differential hops = 7 (default)
    Global hyperparameter controlling the number of views; the paper reports robustness for K around 5-7, and K=0 collapses to the original Dink-Net. Selected by the authors rather than derived.
assumptions (4)
  • domain assumption Homophily: nodes with similar attributes tend to be connected, so same-hop neighborhoods yield similar features across views.
    Appendix B: the consistency of multi-view representations is grounded in the homophily assumption.
  • ad hoc to paper Mean aggregation of features within each differential hop neighborhood retains enough information to distinguish clusters.
    Eq. (3) defines the view as the mean feature vector; the paper does not prove that mean pooling preserves cluster-discriminative structure, especially with imputed features.
  • standard math Standard BFS and set-difference graph operations correctly compute exact-hop neighborhoods on undirected graphs.
    Definitions 1-2 and Algorithm 1 rely on shortest-path distances and breadth-first expansion.
  • domain assumption FP imputation produces features accurate enough for downstream clustering.
    Section 4.2: all methods, including CMV-ND, operate on graphs imputed by FP; if FP fails, CMV-ND views built on imputed features would carry noise.

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Pith. "Pith review of Scalable Attribute-Missing Graph Clustering via Neighborhood Differentiation." pith.science (2026). https://pith.science/paper/XWXVUVX5

@misc{pith2026250713368,
  author       = {Pith},
  title        = {Pith review of: Scalable Attribute-Missing Graph Clustering via Neighborhood Differentiation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XWXVUVX5}},
  note         = {Machine review of arXiv:2507.13368}
}
abstract

Deep graph clustering (DGC), which aims to unsupervisedly separate the nodes in an attribute graph into different clusters, has seen substantial potential in various industrial scenarios like community detection and recommendation. However, the real-world attribute graphs, e.g., social networks interactions, are usually large-scale and attribute-missing. To solve these two problems, we propose a novel DGC method termed \underline{\textbf{C}}omplementary \underline{\textbf{M}}ulti-\underline{\textbf{V}}iew \underline{\textbf{N}}eighborhood \underline{\textbf{D}}ifferentiation (\textit{CMV-ND}), which preprocesses graph structural information into multiple views in a complete but non-redundant manner. First, to ensure completeness of the structural information, we propose a recursive neighborhood search that recursively explores the local structure of the graph by completely expanding node neighborhoods across different hop distances. Second, to eliminate the redundancy between neighborhoods at different hops, we introduce a neighborhood differential strategy that ensures no overlapping nodes between the differential hop representations. Then, we construct $K+1$ complementary views from the $K$ differential hop representations and the features of the target node. Last, we apply existing multi-view clustering or DGC methods to the views. Experimental results on six widely used graph datasets demonstrate that CMV-ND significantly improves the performance of various methods.

Figures

Figures reproduced from arXiv: 2507.13368 by the authors.

Figure 1
Figure 1. Overall workflow of the CMV-ND algorithm. The algorithm recursively searches the neighborhood of each node, treating each neighborhood as a distinct view. Each view contains complete nodes that do not overlap with other views. step, new nodes are added to the neighborhood by exploring all neighbors of the previously discovered nodes, ensuring that nodes are processed layer by layer. The search halts once the k-hop n… view at source ↗
Figure 2
Figure 2. T-SNE visualization of node representations generated by six methods on the Co.CS dataset. (Van der Maaten & Hinton, 2008) to visualize the node representations generated by six baseline algorithms before and after leveraging our CMV-ND. As shown in [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. The hyperparameter analysis of CMV-ND, where the horizontal axis denotes the number of differential hops K. Notably, K = 0 corresponds to the original Dink-Net without CMV-ND. As shown in [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

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Reference graph

Works this paper leans on

68 extracted references · 59 canonical work pages

  1. [1]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION format.date year duplicate empty "emp...

  2. [2]

    Fastgcn: Fast learning with graph convolutional networks via importance sampling

    Chen, J., Ma, T., and Xiao, C. Fastgcn: Fast learning with graph convolutional networks via importance sampling. In Proceedings of the International Conference on Learning Representations, 2018

    work page 2018

  3. [3]

    Chen, X., Chen, S., Yao, J., Zheng, H., Zhang, Y., and Tsang, I. W. Learning on attribute-missing graphs. IEEE transactions on pattern analysis and machine intelligence, 44 0 (2): 0 740--757, 2020

    work page 2020

  4. [4]

    Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks

    Chiang, W.-L., Liu, X., Si, S., Li, Y., Bengio, S., and Hsieh, C.-J. Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp.\ 257--266, 2019

    work page 2019

  5. [5]

    Dayan, P., Kakade, S., and Montague, P. R. Learning and selective attention. Nature neuroscience, 3 0 (11): 0 1218--1223, 2000

    work page 2000

  6. [6]

    S3gc: scalable self-supervised graph clustering

    Devvrit, F., Sinha, A., Dhillon, I., and Jain, P. S3gc: scalable self-supervised graph clustering. Advances in Neural Information Processing Systems, 35: 0 3248--3261, 2022

    work page 2022

  7. [7]

    R., and Jin, H

    Ding, X., Wang, C., Choo, K.-K. R., and Jin, H. A novel privacy preserving framework for large scale graph data publishing. IEEE transactions on knowledge and data engineering, 33 0 (2): 0 331--343, 2019

    work page 2019

  8. [8]

    Explicit view-labels matter: A multifacet complementarity study of multi-view clustering

    Geng, C., Han, A., and Chen, S. Explicit view-labels matter: A multifacet complementarity study of multi-view clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024

    work page 2024

Show all 68 references
  1. [9]

    Attributed graph clustering with dual redundancy reduction

    Gong, L., Zhou, S., Tu, W., and Liu, X. Attributed graph clustering with dual redundancy reduction. In Proceedings of the International Joint Conference on Artificial Intelligence, pp.\ 3015--3021, 2022

    2022

  2. [10]

    Deep fusion clustering network with reliable structure preservation

    Gong, L., Tu, W., Zhou, S., Zhao, L., Liu, Z., and Liu, X. Deep fusion clustering network with reliable structure preservation. IEEE Transactions on Neural Networks and Learning Systems, 35 0 (6): 0 7792--7803, 2024

    2024

  3. [11]

    Bootstrap your own latent-a new approach to self-supervised learning

    Grill, J.-B., Strub, F., Altch \'e , F., Tallec, C., Richemond, P., Buchatskaya, E., Doersch, C., Avila Pires, B., Guo, Z., Gheshlaghi Azar, M., et al. Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems, 33: ...

    2020

  4. [12]

    Structure-adaptive multi-view graph clustering for remote sensing data

    Guan, R., Tu, W., Wang, S., Liu, J., Hu, D., Tang, C., Feng, Y., Li, J., Xiao, B., and Liu, X. Structure-adaptive multi-view graph clustering for remote sensing data. In Proceedings of the AAAI Conference on Artificial Intelligence, pp.\ 16933--16941, 2025

    2025

  5. [13]

    Inductive representation learning on large graphs

    Hamilton, W., Ying, Z., and Leskovec, J. Inductive representation learning on large graphs. Advances in neural information processing systems, 30, 2017

    2017

  6. [14]

    and Khasahmadi, A

    Hassani, K. and Khasahmadi, A. H. Contrastive multi-view representation learning on graphs. In Proceedings of the International Conference on Machine Learning, pp.\ 4116--4126, 2020

    2020

  7. [15]

    T2-gnn: Graph neural networks for graphs with incomplete features and structure via teacher-student distillation

    Huo, C., Jin, D., Li, Y., He, D., Yang, Y.-B., and Wu, L. T2-gnn: Graph neural networks for graphs with incomplete features and structure via teacher-student distillation. Proceedings of the AAAI Conference on Artificial Intelligence, 37 0 (4): 0 4339--4346, 2023

    2023

  8. [16]

    and Zhang, Z

    Jiang, B. and Zhang, Z. Incomplete graph representation and learning via partial graph neural networks. arXiv preprint arXiv:2003.10130, 2020

    2003 arXiv

  9. [17]

    A survey of graph neural networks in real world: Imbalance, noise, privacy and ood challenges

    Ju, W., Yi, S., Wang, Y., Xiao, Z., Mao, Z., Li, H., Gu, Y., Qin, Y., Yin, N., Wang, S., et al. A survey of graph neural networks in real world: Imbalance, noise, privacy and ood challenges. In arXiv preprint arXiv:2403.04468, 2024

    2024

  10. [18]

    Csat: Contrastive sampling-aggregating transformer for community detection in attribute-missing networks

    Li, M., Zhang, Y., Zhang, W., Zhao, S., Piao, X., and Yin, B. Csat: Contrastive sampling-aggregating transformer for community detection in attribute-missing networks. IEEE Transactions on Computational Social Systems, 11 0 (2): 0 2277--2290, 2023

    2023

  11. [19]

    L., Gupta, V., Bhalerao, O., and Lim, S

    Lim, D., Hohne, F., Li, X., Huang, S. L., Gupta, V., Bhalerao, O., and Lim, S. N. Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods. Advances in Neural Information Processing Systems, 34: 0 20887--20902, 2021

    2021

  12. [20]

    Federated graph-level clustering network

    Liu, J., Cheng, J., Han, R., Tu, W., Wang, J., and Peng, X. Federated graph-level clustering network. In Proceedings of the AAAI Conference on Artificial Intelligence, pp.\ 18870--18878, 2025

    2025

  13. [21]

    Deep graph clustering via dual correlation reduction

    Liu, Y., Tu, W., Zhou, S., Liu, X., Song, L., Yang, X., and Zhu, E. Deep graph clustering via dual correlation reduction. Proceedings of the AAAI Conference on Artificial Intelligence, 36 0 (7): 0 7603--7611, 2022 a

    2022

  14. [22]

    Z., Liu, X., and He, K

    Liu, Y., Xia, J., Zhou, S., Yang, X., Liang, K., Fan, C., Zhuang, Y., Li, S. Z., Liu, X., and He, K. A survey of deep graph clustering: Taxonomy, challenge, application, and open resource. arXiv preprint arXiv:2211.12875, 2022 b

    2022 arXiv

  15. [23]

    Towards unsupervised deep graph structure learning

    Liu, Y., Zheng, Y., Zhang, D., Chen, H., Peng, H., and Pan, S. Towards unsupervised deep graph structure learning. In Proceedings of the ACM Web Conference, pp.\ 1392--1403, 2022 c

    2022

  16. [24]

    Liu, Y., Liang, K., Xia, J., Yang, X., Zhou, S., Liu, M., Liu, X., and Li, S. Z. Reinforcement graph clustering with unknown cluster number. In Proceedings of the ACM International Conference on Multimedia, pp.\ 3528--3537, 2023 a

    2023

  17. [25]

    Liu, Y., Liang, K., Xia, J., Zhou, S., Yang, X., , Liu, X., and Li, Z. S. Dink-net: Neural clustering on large graphs. In Proceedings of the International Conference on Machine Learning, 2023 b

    2023

  18. [26]

    Simple contrastive graph clustering

    Liu, Y., Yang, X., Zhou, S., and Liu, X. Simple contrastive graph clustering. IEEE Transactions on Neural Networks and Learning Systems, 2023 c

    2023

  19. [27]

    Hard sample aware network for contrastive deep graph clustering

    Liu, Y., Yang, X., Zhou, S., Liu, X., Wang, Z., Liang, K., Tu, W., Li, L., Duan, J., and Chen, C. Hard sample aware network for contrastive deep graph clustering. In Proceedings of the AAAI Conference on Artificial Intelligence, 2023 d

    2023

  20. [28]

    Improved dual correlation reduction network

    Liu, Y., Zhou, S., Yang, X., Liu, X., Tu, W., Li, L., Xu, X., and Sun, F. Improved dual correlation reduction network. IEEE Transactions on Neural Networks and Learning Systems, 2024 a

    2024

  21. [29]

    End-to-end learnable clustering for intent learning in recommendation

    Liu, Y., Zhu, S., Xia, J., Ma, Y., Ma, J., Zhong, W., Liu, X., Yu, S., and Zhang, K. End-to-end learnable clustering for intent learning in recommendation. In Advances in Neural Information Processing Systems, 2024 b

    2024

  22. [30]

    Identify then recommend: Towards unsupervised group recommendation

    Liu, Y., Zhu, S., Yang, T., Ma, J., and Zhong, W. Identify then recommend: Towards unsupervised group recommendation. In Advances in Neural Information Processing Systems, 2024 c

    2024

  23. [31]

    Learning graph embedding with adversarial training methods

    Pan, S., Hu, R., Fung, S.-f., Long, G., Jiang, J., and Zhang, C. Learning graph embedding with adversarial training methods. IEEE transactions on cybernetics, 50 0 (6): 0 2475--2487, 2019

    2019

  24. [32]

    J., Lee, K., and Choi, J

    Park, J., Lee, M., Chang, H. J., Lee, K., and Choi, J. Y. Symmetric graph convolutional autoencoder for unsupervised graph representation learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.\ 6519--6528, 2019

    2019

  25. [33]

    A., Kim, S., Du, F., Ahmed, N., and Faloutsos, C

    Park, N., Rossi, R., Koh, E., Burhanuddin, I. A., Kim, S., Du, F., Ahmed, N., and Faloutsos, C. Cgc: Contrastive graph clustering forcommunity detection and tracking. In Proceedings of the ACM Web Conference, pp.\ 1115--1126, 2022

    2022

  26. [34]

    Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization

    Sun, F.-Y., Hoffmann, J., Verma, V., and Tang, J. Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. In arXiv preprint arXiv:1908.01000, 2019

    1908 arXiv

  27. [35]

    Sun, Q., Li, J., Peng, H., Wu, J., Fu, X., Ji, C., and Yu, P. S. Graph structure learning with variational information bottleneck. Proceedings of the AAAI Conference on Artificial Intelligence, 36 0 (4): 0 4165--4174, 2022

    2022

  28. [36]

    Graph convolutional networks for graphs containing missing features

    Taguchi, H., Liu, X., and Murata, T. Graph convolutional networks for graphs containing missing features. Future Generation Computer Systems, 117: 0 155--168, 2021

    2021

  29. [37]

    G., Azabou, M., Dyer, E

    Thakoor, S., Tallec, C., Azar, M. G., Azabou, M., Dyer, E. L., Munos, R., Veli c kovi \'c , P., and Valko, M. Large-scale representation learning on graphs via bootstrapping. In Proceedings of the International Conference on Learning Representations, 2022

    2022

  30. [38]

    Deep fusion clustering network

    Tu, W., Zhou, S., Liu, X., Guo, X., Cai, Z., Zhu, E., and Cheng, J. Deep fusion clustering network. In Proceedings of the AAAI Conference on Artificial Intelligence, pp.\ 9978--9987, 2021

    2021

  31. [39]

    Initializing then refining: A simple graph attribute imputation network

    Tu, W., Zhou, S., Liu, X., Liu, Y., Cai, Z., Zhu, E., Zhang, C., and Cheng, J. Initializing then refining: A simple graph attribute imputation network. In Proceedings of the International Joint Conference on Artificial Intelligence, pp.\ 3494--3500, 2022

    2022

  32. [40]

    Attribute-missing graph clustering network

    Tu, W., Guan, R., Zhou, S., Ma, C., Peng, X., Cai, Z., Liu, Z., Cheng, J., and Liu, X. Attribute-missing graph clustering network. Proceedings of the AAAI Conference on Artificial Intelligence, 38 0 (14): 0 15392--15401, 2024 a

    2024

  33. [41]

    Rare: Robust masked graph autoencoder

    Tu, W., Liao, Q., Zhou, S., Peng, X., Ma, C., Liu, Z., Liu, X., Cai, Z., and He, K. Rare: Robust masked graph autoencoder. IEEE Transactions on Knowledge and Data Engineering, 36 0 (10): 0 5340--5353, 2024 b

    2024

  34. [42]

    Hierarchically contrastive hard sample mining for graph self-supervised pre-training

    Tu, W., Zhou, S., Liu, X., Ge, C., Cai, Z., and Liu, Y. Hierarchically contrastive hard sample mining for graph self-supervised pre-training. IEEE Transactions on Neural Networks and Learning Systems, 35 0 (11): 0 16748--16761, 2024 c

    2024

  35. [43]

    Revisiting initializing then refining: An incomplete and missing graph imputation network

    Tu, W., Xiao, B., Liu, X., Zhou, S., Cai, Z., and Cheng, J. Revisiting initializing then refining: An incomplete and missing graph imputation network. IEEE Transactions on Neural Networks and Learning Systems, 36 0 (3): 0 3244--3257, 2025 a

    2025

  36. [44]

    Wage: Weight-sharing attribute-missing graph autoencoder

    Tu, W., Zhou, S., Liu, X., Cai, Z., Zhao, Y., Liu, Y., and He, K. Wage: Weight-sharing attribute-missing graph autoencoder. IEEE Transactions on Pattern Analysis and Machine Intelligence, pp.\ 1--18, 2025 b

    2025

  37. [45]

    Confidence-based feature imputation for graphs with partially known features

    Um, D., Park, J., Park, S., and young Choi, J. Confidence-based feature imputation for graphs with partially known features. In Proceedings of the International Conference on Learning Representations, 2023

    2023

  38. [46]

    and Hinton, G

    Van der Maaten, L. and Hinton, G. Visualizing data using t-sne. Journal of machine learning research, 9 0 (11), 2008

    2008

  39. [47]

    L., Li \`o , P., Bengio, Y., and Hjelm, R

    Velickovic, P., Fedus, W., Hamilton, W. L., Li \`o , P., Bengio, Y., and Hjelm, R. D. Deep graph infomax. ICLR (Poster), 2 0 (3): 0 4, 2019

    2019

  40. [48]

    Continual multi-view clustering

    Wan, X., Liu, J., Liang, W., Liu, X., Wen, Y., and Zhu, E. Continual multi-view clustering. In Proceedings of the ACM International Conference on Multimedia, pp.\ 3676–3684, 2022

    2022

  41. [49]

    One-step multi-view clustering with diverse representation

    Wan, X., Liu, J., Gan, X., Liu, X., Wang, S., Wen, Y., Wan, T., and Zhu, E. One-step multi-view clustering with diverse representation. IEEE Transactions on Neural Networks and Learning Systems, 36 0 (3): 0 1--13, 2024 a

    2024

  42. [50]

    Contrastive continual multiview clustering with filtered structural fusion

    Wan, X., Liu, J., Yu, H., Qu, Q., Li, A., Liu, X., Liang, K., Dong, Z., and Zhu, E. Contrastive continual multiview clustering with filtered structural fusion. IEEE Transactions on Neural Networks and Learning Systems, 2024 b

    2024

  43. [51]

    Fast continual multi-view clustering with incomplete views

    Wan, X., Xiao, B., Liu, X., Liu, J., Liang, W., and Zhu, E. Fast continual multi-view clustering with incomplete views. IEEE Transactions on Image Processing, 33: 0 2995--3008, 2024 c

    2024

  44. [52]

    Simplifying graph convolutional networks

    Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., and Weinberger, K. Simplifying graph convolutional networks. In Proceedings of the International Conference on Machine Learning, pp.\ 6861--6871, 2019

    2019

  45. [53]

    Nodeformer: A scalable graph structure learning transformer for node classification

    Wu, Q., Zhao, W., Li, Z., Wipf, D., and Yan, J. Nodeformer: A scalable graph structure learning transformer for node classification. In Advances in Neural Information Processing Systems, volume 35, pp.\ 22226--22239, 2022

    2022

  46. [54]

    Xia, J., Wu, L., Wang, G., Chen, J., and Li, S. Z. Progcl: Rethinking hard negative mining in graph contrastive learning. In Proceedings of the International Conference on Machine Learning, pp.\ 24332--24346. PMLR, 2022

    2022

  47. [55]

    Deep incomplete multi-view clustering via mining cluster complementarity

    Xu, J., Li, C., Ren, Y., Peng, L., Mo, Y., Shi, X., and Zhu, X. Deep incomplete multi-view clustering via mining cluster complementarity. Proceedings of the AAAI conference on artificial intelligence, 36 0 (8): 0 8761--8769, 2022 a

    2022

  48. [56]

    Y., and He, L

    Xu, J., Ren, Y., Tang, H., Yang, Z., Pan, L., Yang, Y., Pu, X., Philip, S. Y., and He, L. Self-supervised discriminative feature learning for deep multi-view clustering. IEEE Transactions on Knowledge and Data Engineering, 35 0 (7): 0 7470--7482, 2022 b

    2022

  49. [57]

    Multi-level feature learning for contrastive multi-view clustering

    Xu, J., Tang, H., Ren, Y., Peng, L., Zhu, X., and He, L. Multi-level feature learning for contrastive multi-view clustering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.\ 16051--16060, 2022 c

    2022

  50. [58]

    Repbin: constraint-based graph representation learning for metagenomic binning

    Xue, H., Mallawaarachchi, V., Zhang, Y., Rajan, V., and Lin, Y. Repbin: constraint-based graph representation learning for metagenomic binning. Proceedings of the AAAI Conference on Artificial Intelligence, 36 0 (4): 0 4637--4645, 2022

    2022

  51. [59]

    Cluster-guided contrastive graph clustering network

    Yang, X., Liu, Y., Zhou, S., Wang, S., Tu, W., Zheng, Q., Liu, X., Fang, L., and Zhu, E. Cluster-guided contrastive graph clustering network. Proceedings of the AAAI conference on artificial intelligence, 37 0 (9): 0 10834--10842, 2023 a

    2023

  52. [60]

    Z., Liu, X., and Zhu, E

    Yang, X., Tan, C., Liu, Y., Liang, K., Wang, S., Zhou, S., Xia, J., Li, S. Z., Liu, X., and Zhu, E. Convert: Contrastive graph clustering with reliable augmentation. In Proceedings of the ACM International Conference on Multimedia, pp.\ 319--327, 2023 b

    2023

  53. [61]

    Mixed graph contrastive network for semi-supervised node classification

    Yang, X., Wang, Y., Liu, Y., Wen, Y., Meng, L., Zhou, S., Liu, X., and Zhu, E. Mixed graph contrastive network for semi-supervised node classification. ACM Transactions on Knowledge Discovery from Data, 2024

    2024

  54. [62]

    Dynamic spiking graph neural networks

    Yin, N., Wang, M., Chen, Z., De Masi, G., Xiong, H., and Gu, B. Dynamic spiking graph neural networks. Proceedings of the AAAI Conference on Artificial Intelligence, 38 0 (15): 0 16495--16503, 2024

    2024

  55. [63]

    Accurate node feature estimation with structured variational graph autoencoder

    Yoo, J., Jeon, H., Jung, J., and Kang, U. Accurate node feature estimation with structured variational graph autoencoder. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp.\ 2336--2346, 2022

    2022

  56. [64]

    Sparse low-rank multi-view subspace clustering with consensus anchors and unified bipartite graph

    Yu, S., Liu, S., Wang, S., Tang, C., et al. Sparse low-rank multi-view subspace clustering with consensus anchors and unified bipartite graph. IEEE Transactions on Neural Networks and Learning Systems, 2023

    2023

  57. [65]

    Towards resource-friendly, extensible and stable incomplete multi-view clustering

    Yu, S., Dong, Z., Wang, S., Wan, X., Liu, Y., Liang, W., Zhang, P., Tu, W., and Liu, X. Towards resource-friendly, extensible and stable incomplete multi-view clustering. In Proceedings of the International Conference on Machine Learning, 2024 a

    2024

  58. [66]

    A non-parametric graph clustering framework for multi-view data

    Yu, S., Wang, S., Dong, Z., Tu, W., Liu, S., Lv, Z., Li, P., Wang, M., and Zhu, E. A non-parametric graph clustering framework for multi-view data. Proceedings of the AAAI conference on artificial intelligence, 38 0 (15): 0 16558--16567, 2024 b

    2024

  59. [67]

    Graphsaint: Graph sampling based inductive learning method

    Zeng, H., Zhou, H., Srivastava, A., Kannan, R., and Prasanna, V. Graphsaint: Graph sampling based inductive learning method. arXiv preprint arXiv:1907.04931, 2019

    1907 arXiv

  60. [68]

    Deep graph contrastive representation learning

    Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., and Wang, L. Deep graph contrastive representation learning. arXiv preprint arXiv:2006.04131, 2020

    2006 arXiv

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.