pith. sign in

arxiv: 2605.00390 · v1 · submitted 2026-05-01 · 💻 cs.LG

Towards Robust and Scalable Density-based Clustering via Graph Propagation

Yingtao Zheng , Hugo Phibbs , Ninh Pham This is my paper

Pith reviewed 2026-05-09 20:27 UTC · model grok-4.3

classification 💻 cs.LG
keywords density-based clusteringgraph propagationlabel propagationscalable clusteringhigh-dimensional dataneighborhood graphsparameter sensitivity
0
0 comments X

The pith

CluProp recasts density-based clustering as label propagation on neighborhood graphs to reduce parameter sensitivity.

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

The paper presents CluProp as a framework that turns varied-density clustering in high-dimensional spaces into a label propagation process over neighborhood graphs. It connects density-based clustering directly to graph connectivity ideas and uses propagation techniques to lessen the parameter tuning problems common in traditional density methods. A deterministic propagation strategy handles neighborhood identification at scale, and the method works regardless of the distance metric chosen. Tests show it processes millions of points in minutes and beats existing approaches on accuracy for large data.

Core claim

CluProp reimagines varied-density clustering in high-dimensional spaces as a label propagation process over neighborhood graphs. The approach bridges density-based clustering and graph connectivity by applying efficient propagation mechanisms from network science, using a deterministic density-based propagation strategy for scalable neighborhood identification. The framework remains agnostic to distance metrics and delivers better accuracy on large-scale data while handling millions of points efficiently.

What carries the argument

The deterministic density-based propagation strategy on neighborhood graphs that performs label propagation to identify clusters.

If this is right

  • Clustering becomes possible with less parameter tuning across varied densities.
  • The method scales to process millions of points in minutes on standard hardware.
  • Performance holds across any chosen distance metric without modification.
  • Accuracy improves over standard density-based baselines on large data.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The graph view may allow direct combination with existing network algorithms for tasks like community detection in the same data.
  • If propagation can be made incremental, the approach could extend to streaming or online clustering settings.
  • The bridging idea suggests similar propagation tactics might simplify other clustering families that currently need heavy tuning.

Load-bearing premise

A deterministic density-based propagation strategy on neighborhood graphs will reliably identify clusters and scale without creating new parameter problems or failing on real high-dimensional data.

What would settle it

A test on a real high-dimensional dataset with millions of points where CluProp either misidentifies clusters at scale or requires extra tuning beyond the claimed single strategy.

Figures

Figures reproduced from arXiv: 2605.00390 by Hugo Phibbs, Ninh Pham, Yingtao Zheng.

Figure 1
Figure 1. Figure 1: The comparison of AMI provided by CluProp (NNDescent and exact [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The comparison of AMI provided by CluProp (NNDescent for graph constructions with Leiden and DANE) [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: An illustration of DANE. B. Proof of Theorem 2 From Theorem 3.1 (Brito et al., 1997), for k ≥ c log n, the mutual kNN graph is connected within each compact, connected component of a level set Lfi with high probability. Given a core point x ∈ Li , we have dk(x) ≤ εi . Let Vd be the volume of the unit ball in R d , by using the kNN density estimator (Devroye et al., 2013): fbk(x) = k nVd (dk(x))d , we have … view at source ↗
Figure 4
Figure 4. Figure 4: The comparison of AMI provided by CluProp (NNDescent and exact [PITH_FULL_IMAGE:figures/full_fig_p016_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The comparison of AMI provided by CluProp with different parameters of PyNNDescent for graph constructions [PITH_FULL_IMAGE:figures/full_fig_p017_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The AMI and runtime of CluProp with Leiden and DANE across various values of [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗
read the original abstract

We present \textit{CluProp}, a novel framework that reimagines varied-density clustering in high-dimensional spaces as a label propagation process over neighborhood graphs. Our approach formally bridges the gap between density-based clustering and graph connectivity, leveraging efficient propagation mechanisms from network science to mitigate the parameter sensitivity inherent in traditional density-based methods. Specifically, we introduce a deterministic density-based propagation strategy to ensure scalable neighborhood identification. The framework is agnostic to the choice of distance metric and exhibits superior performance on large-scale data, processing millions of points in minutes while consistently outperforming existing baselines in accuracy.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript presents CluProp, a framework that reimagines density-based clustering in high-dimensional spaces as a label propagation process over neighborhood graphs. It claims to formally bridge density-based clustering and graph connectivity via efficient propagation mechanisms from network science, introduce a deterministic density-based propagation strategy to reduce parameter sensitivity, remain agnostic to distance metrics, and deliver superior accuracy and scalability on large-scale data (millions of points in minutes) compared to baselines.

Significance. If the formal bridge and empirical claims hold, the work could meaningfully advance scalable clustering by integrating graph propagation ideas to address parameter sensitivity in methods like DBSCAN, with potential impact on high-dimensional ML applications. The deterministic and metric-agnostic aspects would be particularly valuable strengths if rigorously shown.

major comments (2)
  1. [Abstract] Abstract: The central claim that the approach 'formally bridges the gap between density-based clustering and graph connectivity' is unsupported by any theorem, proposition, derivation, or equation establishing equivalence or property preservation (e.g., that propagation recovers core/border partitions or level-set connectivity from density estimation). This is load-bearing for the paper's primary contribution.
  2. [Abstract] Abstract: Assertions of processing millions of points in minutes while 'consistently outperforming existing baselines in accuracy' are presented without any experimental setup, results, tables, figures, error analysis, or dataset descriptions, preventing evaluation of the scalability and performance claims.
minor comments (1)
  1. [Abstract] The abstract refers to 'efficient propagation mechanisms from network science' and a 'deterministic density-based propagation strategy' without naming the specific mechanisms or providing pseudocode/definitions, which reduces clarity on how parameter sensitivity is mitigated.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments on our manuscript. We have addressed the concerns about the abstract by revising the wording of key claims to ensure they accurately reflect the content and support provided in the full paper, while preserving the intended contribution.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that the approach 'formally bridges the gap between density-based clustering and graph connectivity' is unsupported by any theorem, proposition, derivation, or equation establishing equivalence or property preservation (e.g., that propagation recovers core/border partitions or level-set connectivity from density estimation). This is load-bearing for the paper's primary contribution.

    Authors: We acknowledge the referee's point that the phrasing 'formally bridges' may imply a strict mathematical equivalence or theorem that is not present. The manuscript establishes the connection algorithmically by defining a deterministic propagation rule on neighborhood graphs derived from density estimates, which preserves the connectivity properties of density-based clusters (as detailed in the method section through the propagation mechanics and their relation to core points and borders). To address this precisely, we will revise the abstract to state that the approach 'establishes a connection between density-based clustering and graph connectivity via label propagation' and add a cross-reference to the relevant algorithmic derivation. No new theorem will be introduced, as the contribution centers on the practical reframing and its empirical benefits. revision: yes

  2. Referee: [Abstract] Abstract: Assertions of processing millions of points in minutes while 'consistently outperforming existing baselines in accuracy' are presented without any experimental setup, results, tables, figures, error analysis, or dataset descriptions, preventing evaluation of the scalability and performance claims.

    Authors: The abstract provides a concise summary of the empirical findings, with the full experimental setup, datasets, tables, figures, and analysis (including runtime on millions of points and accuracy comparisons) detailed in the Experiments section. We agree that a pointer would aid evaluation. We will revise the abstract to include a brief reference such as '(as shown in our large-scale experiments)' to direct readers to the supporting results without expanding the abstract length. revision: yes

Circularity Check

0 steps flagged

No circularity: claimed bridge presented without self-referential reduction

full rationale

The abstract asserts a formal bridge between density-based clustering and graph propagation via a deterministic strategy, but supplies no equations, propositions, or self-citations that reduce the claimed equivalence to a definition, fit, or prior author result by construction. No load-bearing step is exhibited that renames a fitted parameter as a prediction or imports uniqueness from the authors' own prior work. The framework is described at a high level as reimagining clustering as label propagation, yet the provided text contains no derivation chain that loops back to its inputs; the central claim therefore remains an independent proposal rather than a tautology.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; the central claim rests on an unspecified deterministic propagation rule whose details are not given.

pith-pipeline@v0.9.0 · 5385 in / 1060 out tokens · 35588 ms · 2026-05-09T20:27:41.412686+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

65 extracted references · 65 canonical work pages

  1. [1]

    Ultra-Scalable Spectral Clustering and Ensemble Clustering , journal =

    Dong Huang and Chang. Ultra-Scalable Spectral Clustering and Ensemble Clustering , journal =

    work page

  2. [2]

    Information Sciences , volume=

    Automatic topography of high-dimensional data sets by non-parametric density peak clustering , author=. Information Sciences , volume=. 2021 , publisher=

    work page 2021

  3. [3]

    Reid Andersen and Fan R. K. Chung and Kevin J. Lang , title =. 47th Annual

    work page

  4. [4]

    CoRR , year =

    Vladimir Batagelj and Matjaz Zaversnik , title =. CoRR , year =

    work page

  5. [5]

    ACM Transactions on Database Systems (TODS) , volume=

    On the hardness and approximation of Euclidean DBSCAN , author=. ACM Transactions on Database Systems (TODS) , volume=

    work page

  6. [6]

    Proceedings of the 2021 International Conference on Management of Data , pages=

    Fast density-peaks clustering: multicore-based parallelization approach , author=. Proceedings of the 2021 International Conference on Management of Data , pages=

    work page 2021

  7. [7]

    Knowledge-Based Systems , volume=

    Adaptive density peak clustering based on K-nearest neighbors with aggregating strategy , author=. Knowledge-Based Systems , volume=

    work page

  8. [8]

    2022 IEEE International Conference on Data Mining (ICDM) , pages=

    DBHD: Density-based clustering for highly varying density , author=. 2022 IEEE International Conference on Data Mining (ICDM) , pages=

    work page 2022

  9. [9]

    Ricardo J. G. B. Campello and Davoud Moulavi and Arthur Zimek and J. Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection , journal =

    work page

  10. [10]

    Knowledge and Information Systems , volume=

    A novel density peaks clustering with sensitivity of local density and density-adaptive metric , author=. Knowledge and Information Systems , volume=

    work page

  11. [11]

    Physical Review E—Statistical, Nonlinear, and Soft Matter Physics , volume=

    Narrow scope for resolution-limit-free community detection , author=. Physical Review E—Statistical, Nonlinear, and Soft Matter Physics , volume=

    work page

  12. [12]

    High-dimensional density-based clustering using locality-sensitive hashing , booktitle =

    Camilla Birch Okkels and Martin Aum. High-dimensional density-based clustering using locality-sensitive hashing , booktitle =

    work page

  13. [13]

    , author=

    Improving the cluster structure extracted from optics plots. , author=. LWDA , pages=

    work page

  14. [14]

    Applied Soft Computing , volume=

    DPC-DNG: Graph-based label propagation of k-nearest higher-density neighbors for density peaks clustering , author=. Applied Soft Computing , volume=. 2024 , publisher=

    work page 2024

  15. [15]

    Density Peaks Clustering Based on Label Propagation and K-Mutual-Nearest Neighbors , year=

    Sun, Liping and Huang, Fan and Zheng, Xiaoyao and Guo, Liangmin and Yu, Qingying and Chen, Zhenghua and Luo, Yonglong , journal=. Density Peaks Clustering Based on Label Propagation and K-Mutual-Nearest Neighbors , year=

    work page

  16. [16]

    science , volume=

    Clustering by fast search and find of density peaks , author=. science , volume=

    work page

  17. [17]

    Breunig and Hans

    Mihael Ankerst and Markus M. Breunig and Hans

    work page

  18. [18]

    Junhao Gan and Yufei Tao , title =

    work page

  19. [19]

    ICML , pages=

    DBSCAN++: Towards fast and scalable density clustering , author=. ICML , pages=

    work page

  20. [20]

    NeurIPS , year =

    Heinrich Jiang and Jennifer Jang and Jakub Lacki , title =. NeurIPS , year =

    work page

  21. [21]

    Scalable

    Xu, HaoChuan and Pham, Ninh , journal=. Scalable

    work page

  22. [22]

    Data Min

    Johannes Schneider and Michail Vlachos , title =. Data Min. Knowl. Discov. , volume =

    work page

  23. [23]

    A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , booktitle =

    Martin Ester and Hans. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , booktitle =

    work page

  24. [24]

    Erich Schubert and J

    work page

  25. [25]

    Mai and Ira Assent and Martin Storgaard , title =

    Son T. Mai and Ira Assent and Martin Storgaard , title =

    work page

  26. [26]

    SIGMOD , volume =

    Xiaogang Huang and Tiefeng Ma , title =. SIGMOD , volume =

    work page

  27. [27]

    Mahesh Kumar and A

    K. Mahesh Kumar and A. Rama Mohan Reddy , title =. Pattern Recognit. , volume =

    work page

  28. [28]

    Mark de Berg and Ade Gunawan and Marcel Roeloffzen , title =. Int. J. Comput. Geom. Appl. , volume =

    work page

  29. [29]

    Ricardo J. G. B. Campello and Peer Kr. Density-based clustering , journal =

    work page

  30. [30]

    Viswanath and V

    P. Viswanath and V. Suresh Babu , title =. Pattern Recognit. Lett. , volume =

    work page

  31. [31]

    Mirrokni and Peilin Zhong , title =

    Hossein Esfandiari and Vahab S. Mirrokni and Peilin Zhong , title =

    work page

  32. [32]

    International Conference on Machine Learning , pages=

    Density level set estimation on manifolds with dbscan , author=. International Conference on Machine Learning , pages=. 2017 , organization=

    work page 2017

  33. [33]

    IEEE Transactions on Information Theory , volume=

    Consistent procedures for cluster tree estimation and pruning , author=. IEEE Transactions on Information Theory , volume=. 2014 , publisher=

    work page 2014

  34. [34]

    IEEE transactions on information theory , volume=

    Least squares quantization in PCM , author=. IEEE transactions on information theory , volume=

    work page

  35. [35]

    Jianbo Shi and Jitendra Malik , title =

    work page

  36. [36]

    David Arthur and Sergei Vassilvitskii , title =

    work page

  37. [37]

    Dhillon and Yuqiang Guan and Brian Kulis , title =

    Inderjit S. Dhillon and Yuqiang Guan and Brian Kulis , title =

    work page

  38. [38]

    Systematic Analysis of Cluster Similarity Indices: How to Validate Validation Measures , booktitle =

    Martijn G. Systematic Analysis of Cluster Similarity Indices: How to Validate Validation Measures , booktitle =

    work page

  39. [39]

    Mahoney , title =

    Shusen Wang and Alex Gittens and Michael W. Mahoney , title =. J. Mach. Learn. Res. , volume =

    work page

  40. [40]

    international conference on machine learning , pages=

    Towards k-means-friendly spaces: Simultaneous deep learning and clustering , author=. international conference on machine learning , pages=. 2017 , organization=

    work page 2017

  41. [41]

    Proceedings of the AAAI conference on artificial intelligence , pages=

    The spectacl of nonconvex clustering: A spectral approach to density-based clustering , author=. Proceedings of the AAAI conference on artificial intelligence , pages=

    work page

  42. [42]

    Physical Review E—Statistical, Nonlinear, and Soft Matter Physics , volume=

    Near linear time algorithm to detect community structures in large-scale networks , author=. Physical Review E—Statistical, Nonlinear, and Soft Matter Physics , volume=. 2007 , publisher=

    work page 2007

  43. [43]

    Journal of statistical mechanics: theory and experiment , volume=

    Fast unfolding of communities in large networks , author=. Journal of statistical mechanics: theory and experiment , volume=. 2008 , publisher=

    work page 2008

  44. [44]

    Scientific reports , volume=

    From Louvain to Leiden: guaranteeing well-connected communities , author=. Scientific reports , volume=. 2019 , publisher=

    work page 2019

  45. [45]

    Complex syst , volume=

    The igraph software , author=. Complex syst , volume=

    work page

  46. [46]

    Andrea Vedaldi and Andrew Zisserman , title =

    work page

  47. [47]

    NIPS , pages =

    Ali Rahimi and Benjamin Recht , title =. NIPS , pages =

    work page

  48. [48]

    Cynthia Dwork and Aaron Roth , title =. Found. Trends Theor. Comput. Sci. , volume =

    work page

  49. [49]

    Proceedings of the 20th International Conference on World Wide Web (WWW) , year =

    Efficient k-Nearest Neighbor Graph Construction for Generic Similarity Measures , author =. Proceedings of the 20th International Conference on World Wide Web (WWW) , year =

    work page

  50. [50]

    2021 , url=

    McInnes, Leland , title=. 2021 , url=

    work page 2021

  51. [51]

    2024 , eprint=

    The Faiss library , author=. 2024 , eprint=

    work page 2024

  52. [52]

    Malkov and Dmitry A

    Yury A. Malkov and Dmitry A. Yashunin , title =

    work page

  53. [53]

    NeurIPS , year =

    Ninh Pham and Tao Liu , title =. NeurIPS , year =

    work page

  54. [54]

    Razenshteyn and Ludwig Schmidt , title =

    Alexandr Andoni and Piotr Indyk and Thijs Laarhoven and Ilya P. Razenshteyn and Ludwig Schmidt , title =. NIPS , pages =

    work page

  55. [55]

    Geometric Range Searching , journal =

    Jir. Geometric Range Searching , journal =

    work page

  56. [56]

    A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces , booktitle =

    Roger Weber and Hans. A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces , booktitle =

    work page

  57. [57]

    Proceedings of the Twenty-Eighth Annual

    Tobias Christiani , title =. Proceedings of the Twenty-Eighth Annual

    work page

  58. [58]

    Razenshteyn and Erik Waingarten , title =

    Alexandr Andoni and Thijs Laarhoven and Ilya P. Razenshteyn and Erik Waingarten , title =. SODA , pages =

    work page

  59. [59]

    H. A. David and J. Galambos , journal =. The Asymptotic Theory of Concomitants of Order Statistics , volume =

    work page

  60. [60]

    David, H. A. Concomitants of Extreme Order Statistics. Extreme Value Theory and Applications: Proceedings of the Conference on Extreme Value Theory and Applications, Volume 1 Gaithersburg Maryland 1993. 1994

    work page 1993

  61. [61]

    2013 , publisher=

    A probabilistic theory of pattern recognition , author=. 2013 , publisher=

    work page 2013

  62. [62]

    Karger , title =

    David R. Karger , title =. Math. Oper. Res. , volume =

    work page

  63. [63]

    Statistics & Probability Letters , volume=

    Connectivity of the mutual k-nearest-neighbor graph in clustering and outlier detection , author=. Statistics & Probability Letters , volume=. 1997 , publisher=

    work page 1997

  64. [64]

    Proceedings of the national academy of sciences , volume=

    Modularity and community structure in networks , author=. Proceedings of the national academy of sciences , volume=. 2006 , publisher=

    work page 2006

  65. [65]

    2025 , note =

    CUDA C++ Programming Guide , author =. 2025 , note =

    work page 2025