Pith. sign in

REVIEW 4 major objections 4 minor 2 cited by

$\nu$-LPA: Fast GPU-based Label Propagation Algorithm (LPA) for Community Detection

T0 review · 4 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper claims that two GPU-specific changes — a Pick-Less rule every fourth iteration and a per-vertex hashtable with hybrid quadratic-double probing — let label propagation detect communities at 3.0 billion edges per second on an…

desk verdict Solid GPU LPA engineering with impressive speedups, but two concrete hashtable bugs in the pseudocode—undersized capacity for power-of-two degrees and a non-accumulating update—undermine the exact numbers until fixed. read the letter →

arxiv 2411.11468 v2 pith:KXWVAM3W submitted 2024-11-18 cs.DC cs.SI

Subhajit Sahu This is my paper · ORCID
classification cs.DCcs.SI
keywords communitydetectionlabelpropagationalgorithmGPUcomputingCUDAopenaddressingper-vertexhashtablequadratic-doubleprobingmodularity
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

Label propagation is one of the fastest heuristics for finding communities in large networks, but its natural parallel form on GPUs can fail to converge because symmetric vertices keep swapping labels. This paper claims that two changes make it converge and run fast on SIMT hardware: a Pick-Less rule applied every fourth iteration, which only allows a vertex to move to a smaller community ID, and a per-vertex open-addressing hashtable that resolves collisions with a hybrid of quadratic probing and double hashing. On an NVIDIA A100, the resulting implementation, called nu-LPA, processes up to 3.0 billion edges per second on a 2.2 billion edge graph and reports average speedups of 364x over FLPA, 62x over NetworKit LPA, 2.6x over Gunrock LPA, and 37x over cuGraph Louvain. The detected communities have 4.7% higher modularity than FLPA but 6.1% and 9.6% lower modularity than NetworKit LPA and cuGraph Louvain, so the paper's case is that LPA can be made dramatically faster on GPUs at a modest quality cost.

What carries the argument

The load-bearing object is the per-vertex open-addressing hashtable with quadratic-double probing. Each vertex i gets a hashtable of capacity p1 = nextPow2(degree)-1 storing neighbor labels as keys and accumulated edge weights as values; memory is allocated in two flat arrays of size 2|E|, with vertex i's table at offset 2*CSR_offset(i). On a collision, the probe step is the sum of a quadratic component (doubling the delta) and a double-hash component using a secondary prime p2 = nextPow2(p1)-1, which the paper argues balances clustering against cache efficiency. A second mechanism, Pick-Less every 4 iterations, is what prevents community swaps; the paper's experiments choose it over cross-checking and hybrid variants on the basis of relative runtime and modularity, using 32-bit floats for hashtable values and a degree-32 switch between thread-per-vertex and block-per-vertex kernels.

What would settle it

Run nu-LPA on a graph whose vertices all have power-of-two degrees, such as a collection of disjoint 4-, 8-, and 16-vertex cliques with distinct initial labels, and compare the resulting communities against the same algorithm run with tables of capacity nextPow2(D) instead of nextPow2(D)-1; if the two outputs differ or the small-capacity version yields lower modularity, the silent-drop premise is violated.

Watch

Extended reading notes

Core claim

The central claim is that asynchronous label propagation can be made to converge reliably on GPU hardware, and to do so at multi-billion-edge-per-second rates, without a quality collapse. The convergence fix is the Pick-Less (PL) method: every four iterations, a vertex may change its label only to a community ID strictly smaller than its current one, breaking the symmetric label-swap cycles that otherwise keep the algorithm running for all 20 allowed iterations. The speed fix is a global-memory per-vertex hashtable, sized at twice each vertex's degree and given capacity nextPow2(degree)-1, using hybrid quadratic-double probing, plus a thread-per-vertex kernel for low-degree vertices and a block-per-vertex kernel for high-degree ones, and 32-bit floats for accumulated label weights. The paper reports the resulting nu-LPA outperforming FLPA, NetworKit LPA, Gunrock LPA, and cuGraph Louvain by 364x, 62x, 2.6x, and 37x on an A100, processing 3.0B edges/s on the it-2004 graph, while producing 4.7% higher modularity than FLPA but 6.1% and 9.6% lower than NetworKit LPA and cuGraph Louvain.

Load-bearing premise

The collapse point is in Section 4.2 and Algorithm 1: each vertex's hashtable is sized nextPow2(degree)-1, and when degree is a power of two this capacity is one less than the number of neighbors, so a table holding all distinct labels has no empty slot for the last insertion; Algorithm 1 ignores the failed status returned by hashtableAccumulate, and all reported speed and modularity numbers assume this never changes a label.

Editorial extensions

If this is right

  • A single A100 can detect communities on a 2.2-billion-edge graph in about 1.6 seconds, making LPA practical for interactive or repeated partitioning of very large graphs.
  • For applications that can tolerate 6-10% lower modularity, nu-LPA offers a 37x speed advantage over cuGraph Louvain on the tested graphs; for applications that need maximum modularity, Louvain remains the better choice.
  • The per-vertex hashtable design bounds total hashtable memory by O(|E|), so the method scales to graphs whose degree distribution is skewed as long as the edge list fits in GPU memory.
  • The same PL4 symmetry-breaking recipe can be dropped into other asynchronous label-diffusion algorithms on SIMT hardware, not just LPA.

Reading between the lines

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

  • The capacity choice nextPow2(D)-1 makes the modulo hash cheap, but it leaves no empty slot for the last insertion when D is a power of two and all neighbor labels are distinct; a simple fix would be capacity nextPow2(D) with masking, or checking the return status of hashtableAccumulate.
  • The speedups are measured against CPU baselines for FLPA and NetworKit LPA, so those gaps blend hardware and algorithm differences; the GPU-vs-GPU comparisons (2.6x vs Gunrock LPA, 37x vs cuGraph Louvain) are the cleaner speed claims.
  • Because PL4 restricts label movement to lower IDs every fourth iteration, it biases communities toward earlier (smaller-ID) labels; this may explain part of the modularity gap versus NetworKit LPA and suggests testing with randomized initial label orderings to separate bias from quality.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. This report describes ν-LPA, a CUDA implementation of asynchronous label propagation for community detection. The main technical contributions are a per-vertex open-addressing hash table stored in global memory with a hybrid quadratic-double probing collision strategy, a Pick-Less symmetry-breaking rule applied every four iterations, and a two-kernel decomposition that processes low-degree vertices with one thread per vertex and high-degree vertices with one block per vertex. The authors benchmark ν-LPA on 13 SuiteSparse graphs and report large speedups over FLPA, NetworKit LPA, Gunrock LPA, and cuGraph Louvain on an A100 GPU, together with modularity values that are higher than FLPA but lower than NetworKit LPA and cuGraph Louvain.

Significance. If the algorithm and measurements are taken at face value, the paper would be a useful engineering contribution: it gives a concrete GPU LPA design with public code, benchmark results on very large graphs, and a plausible mechanism for convergence problems on SIMT hardware. The per-vertex hash table idea is relevant, and the comparison against several baselines is informative. However, the two algorithmic defects identified below directly affect which labels are computed and therefore the reported modularity and speed numbers; until those defects are resolved, the empirical claims are not supported. The paper's strengths are its clear pseudocode, the public repository link, and the breadth of the experimental dataset.

major comments (4)
  1. [Section 4.2, Algorithm 1 line 19, Algorithm 2 lines 3-18] Algorithm 1 sets p1 = nextPow2(degree(i))-1 and sizes the hash table as H_k[theta_H : theta_H+p1]. For any vertex whose degree is a power of two, p1 = degree-1, so the table has one fewer slot than the number of neighbors. In the first LPA iteration all labels are distinct, so, for example, a degree-2 vertex must insert two distinct keys into a one-slot table. Algorithm 2's probing loop cannot find alternative slots (with p1=1 every slot index is 0), and it returns FAILED after MAX_RETRIES, but Algorithm 1 line 28 ignores this return value. The same issue gives p1=0 for degree-1 vertices, which implies a modulo-by-zero in Algorithm 2 line 4. This directly contradicts the statement in Section 4.2 that the table size 'must be at least as large as the degree' and that the allocation keeps the load factor below 100%. Since degree-2 vertices are abundant in the road and k-mer graphs (average degree 2.1), the reported modularity and throughput numbers may characterize a truncated LPA rather than the algorithm of Eq. (3). The authors should correct the capacity formula or explain how insertion failures are handled and show that they do not affect the reported results.
  2. [Algorithm 2 lines 5-9] In the non-shared branch, when the slot already contains the key, the code executes H_v[s] <- v, which overwrites the previously accumulated weight instead of adding the new value. The shared branch uses atomicAdd, so the two kernels implement different semantics. In the thread-per-vertex kernel (degree < SWITCH_DEGREE = 32), any vertex with two or more neighbors in the same community will have its aggregated label weight replaced by the last edge weight rather than summed, so the argmax required by Eq. (3) is not computed. This is a load-bearing error for all low-degree vertices, and it may explain part of the modularity gap relative to NetworKit LPA. The pseudocode should use H_v[s] <- H_v[s] + v, and the experiments should be rerun if the implementation mirrors the current text.
  3. [Section 5.2, Figures 1, 3, 4] The design parameters (rho = 4, SWITCH_DEGREE = 32, quadratic-double probing) are selected using the same 13 graphs on which the headline results are reported. Figures 1, 3, and 4 show relative runtime or modularity averaged over these graphs, so the final speedups are post-selection values rather than independent predictions. In addition, the speedup averages are computed over different subsets for different baselines because Gunrock LPA and cuGraph Louvain fail on several graphs, but the paper does not state which graphs are included in each average. The authors should either validate the selected parameters on a separate set of graphs or report per-graph results for all configurations, and should state the subset used for every reported average.
  4. [Section 5.2, Gunrock timing] For Gunrock LPA, the authors state that they 'measure the only iteration time using cpu_timer', whereas for the other baselines they measure total end-to-end runtime. Comparing an iteration-only time against full end-to-end time is not an apples-to-apples comparison and can materially affect the reported 2.6x speedup over Gunrock LPA. Please report Gunrock's end-to-end runtime including preprocessing and graph loading, or clearly state that the speedup is for the compute phase only and provide corresponding phase-level timings for ν-LPA.
minor comments (4)
  1. [Section 4.2, Figure 3] The word 'Quadriatic' is misspelled in Section 4.2 and in Figure 3; it should be 'Quadratic'.
  2. [Algorithm 2, Section 4.2] The quantities p1 and p2 are repeatedly described as primes, but p1 = nextPow2(degree)-1 and p2 = nextPow2(p1)-1 are not prime in general (for example, degree 16 gives p1 = 15). The text should call them hash moduli rather than primes, or the formulas should be changed to actually produce primes.
  3. [Table 1] Table 1 has several presentation issues: 'LA W' should be 'LAW' or 'Web Graphs', 'obtained SuiteSparse' is missing 'from', and the graph categories are not consistently labeled.
  4. [Algorithm 2] The value of MAX_RETRIES is never stated, and its relation to p1 is not discussed. Since the pseudocode relies on this bound to decide when to return FAILED, the default value should be given and justified.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the paper's claims are empirical measurements against external baselines, and the LPA update objective is standard rather than derived from its own outputs.

full rationale

The paper's central claims are benchmark measurements: runtimes and modularity of ν-LPA versus FLPA, NetworKit LPA, Gunrock LPA, and cuGraph Louvain, all external implementations with separate code bases. The update rule in Equation 3 is the standard LPA argmax objective, and the reported modularity is computed from the output partition via Equation 1; neither quantity is defined in terms of the speedups. The novel per-vertex hashtable and probing strategy are engineering mechanisms whose quality is judged by measured runtime and resulting modularity, not by an equation that assumes the target result. The Pick-Less cadence (PL4), switch degree (32), and 32-bit float values are selected by experiments on the same 13 graphs, which is a form of benchmark tuning that can inflate reported averages, but this is not circular reasoning: no fitted parameter is renamed as a prediction, and the comparison numbers remain externally measured. Self-citations to GVE-LPA [45] and to the author's earlier LPA selection study [46] supply context and prior-work baselines; the current paper's claims do not reduce to those citations. The contradiction between the 'must be at least as large as the degree' statement and the nextPow2(degree)-1 capacity is a real correctness/soundness issue (Algorithm 1 lines 19-28 and Algorithm 2 line 18 may silently drop keys for power-of-two degrees), but it is a bug risk, not a circularity: it does not make the derivation equivalent to its inputs. Overall, no load-bearing circular step was found.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The central performance claims rest on a small set of tuned hyperparameters and on the correctness of the LPA heuristic and the per-vertex hashtable. The hashtable capacity assumption is the most fragile: it is ad hoc to this paper and is violated for power-of-two degrees. No new physical entities are introduced.

free parameters (5)
  • pick-less period rho = 4
    Chosen by testing CC/PL/H variants every 1 to 4 iterations on all 13 benchmark graphs in Section 4.1 and Figure 1; the final speedup and modularity numbers use this choice on the same graphs.
  • switch degree = 32
    Chosen by testing thresholds 2 to 256 on the same graphs in Section 4.3 and Figure 4.
  • collision probing strategy = quadratic-double
    Selected by comparing four strategies on the same benchmark graphs in Section 4.2 and Figure 3.
  • hashtable capacity factor = 2*degree reserved, nextPow2(degree)-1 slots used
    Design choice to bound memory by O(|E|); the slot formula gives fewer slots than degree for power-of-two degrees, creating a correctness risk.
  • max iterations and tolerance = 20 and 0.05
    Inherited from GVE-LPA and prior LPA work; not varied in this paper but affects runtime and final quality.
assumptions (5)
  • domain assumption LPA's update rule (Equation 3) and termination criterion (fraction of changes below tau) produce useful communities.
    Adopted from Raghavan et al. [40]; the paper does not prove convergence or quality, and relies on the heuristic.
  • domain assumption Modularity (Equation 1) is the appropriate quality metric for comparing community detection outputs.
    Standard in the field; used to interpret the 4.7% higher and 6.1%/9.6% lower comparisons.
  • domain assumption Asynchronous in-place label updates on the GPU converge to a partition within 20 iterations when PL is applied every 4 iterations.
    Assumed from the experimental design in Algorithm 1, lines 4-9; no proof that PL prevents all cycles.
  • ad hoc to paper The per-vertex hashtable with capacity nextPow2(degree)-1 holds all distinct neighbor labels of every vertex.
    This is the paper's specific design in Section 4.2; it fails for power-of-two degrees where capacity is degree-1, and Algorithm 1 ignores failed inserts.
  • domain assumption GPU lockstep execution makes label-swap cycles more likely, motivating the Pick-Less method.
    Stated in Section 4.1; no measurement isolates lockstep as the cause of non-convergence.

how reviews work

0 comments
Cite this review

Pith. "Pith review of $\nu$-LPA: Fast GPU-based Label Propagation Algorithm (LPA) for Community Detection." pith.science (2026). https://pith.science/paper/KXWVAM3W

@misc{pith2026241111468,
  author       = {Pith},
  title        = {Pith review of: $\nu$-LPA: Fast GPU-based Label Propagation Algorithm (LPA) for Community Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KXWVAM3W}},
  note         = {Machine review of arXiv:2411.11468}
}
abstract

Community detection is the problem of identifying natural divisions in networks. Efficient parallel algorithms for identifying such divisions are critical in a number of applications. This report presents an optimized implementation of the Label Propagation Algorithm (LPA) for community detection, featuring an asynchronous LPA with a Pick-Less (PL) method every 4 iterations to handle community swaps, ideal for SIMT hardware like GPUs. It also introduces a novel per-vertex hashtable with hybrid quadratic-double probing for collision resolution. On an NVIDIA A100 GPU, our implementation, $\nu$-LPA, outperforms FLPA (sequential), NetworKit LPA (multicore), Gunrock LPA (GPU), and cuGraph Louvain (GPU) by 364x, 62x, 2.6x, and 37x, respectively, while running FLPA and NetworKit LPA on a server with dual 16-core Intel Xeon Gold 6226R processors - processing 3.0B edges/s on a 2.2B edge graph - and achieves 4.7% higher modularity than FLPA, but 6.1% and 9.6% lower than NetworKit LPA and cuGraph Louvain.

Figures

Figures reproduced from arXiv: 2411.11468 by the authors.

Figure 1
Figure 1. Relative Runtime and Modularity of obtained com [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Illustration of per-vertex open-addressing hashta [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 4
Figure 4. Relative Runtime with various switch degrees, i.e., [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figures from the paper (3 more)
Figure 3
Figure 3. Figure 3: Relative Runtime with using Linear probing, Quadriatic probing, Double hashing, and a hybrid of Quadri￾atic probing and Double hashing (Quadriatic-double) for col￾lision resolution in the per-vertex hashtables. 4.3 Partitioning work between two kernels Processing each …
Figure 5
Figure 5. Figure 5: Relative Runtime with using 32-bit floating point [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Runtime in seconds (log-scale), speedup (log-scale), and modularity of obtained communities with [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CPU vs. GPU for Community Detection: Performance Insights from GVE-Louvain and $\nu$-Louvain

    cs.DC 2025-01 conditional novelty 4.0 of 10

    A tuned multicore CPU implementation of Louvain is claimed to beat leading CPU and GPU implementations on billion-edge graphs, with a GPU version only matching it.

  2. Memory Efficient GPU-based Label Propagation Algorithm (LPA) for Community Detection on Large Graphs

    cs.DC 2024-11 conditional novelty 4.0 of 10

    Replacing per-vertex hash tables with 8-slot Misra-Gries sketches makes GPU label propagation use O(|V|) memory instead of O(|E|), cutting memory up to 98x with roughly 5% modularity loss.

Reference graph

Works this paper leans on

77 extracted references · 31 canonical work pages · cited by 2 Pith papers

  1. [1]

    Emmanuel Abbe. 2018. Community detection and stochastic block models: recent developments. Journal of Machine Learning Research 18, 177 (2018), 1–86

    2018

  2. [2]

    Yaroslav Akhremtsev, Peter Sanders, and Christian Schulz. 2020. High-quality shared-memory graph partitioning. IEEE Transactions on Parallel and Distributed Systems 31, 11 (2020), 2710–2722

    work page 2020

  3. [3]

    Tarique Aziz, Muhammad Waseem, Shengyuan Liu, Zhenzhi Lin, Yuxuan Zhao, and Kaiyuan Pang. 2023. A novel power system sectionalizing strategy based on modified label propagation algorithm. In 2023 6th International Conference on Energy, Electrical and Power Engineering (CEEPE) . IEEE, 807–812

    work page 2023

  4. [4]

    Minho Bae, Minjoong Jeong, and Sangyoon Oh. 2020. Label propagation-based parallel graph partitioning for large-scale graph data. IEEE Access 8 (2020), 72801–72813

    work page 2020

  5. [5]

    Yuhe Bai, Camelia Constantin, and Hubert Naacke. 2024. Leiden-Fusion Parti- tioning Method for Effective Distributed Training of Graph Embeddings. InJoint European Conference on Machine Learning and Knowledge Discovery in Databases . Springer, 366–382

    2024

  6. [6]

    Joel J Bechtel, William A Kelley, Teresa A Coons, M Gerry Klein, Daniel D Slagel, and Thomas L Petty. 2005. Lung cancer detection in patients with airflow obstruction identified in a primary care outpatient practice. Chest 127, 4 (2005), 1140–1145

    work page 2005

  7. [7]

    Kamal Berahmand and Asgarali Bouyer. 2018. LP-LPA: A link influence-based label propagation algorithm for discovering community structures in networks. International Journal of Modern Physics B 32, 06 (2018), 1850062

    work page 2018

  8. [8]

    Blondel, J

    V. Blondel, J. Guillaume, R. Lambiotte, and E. Lefebvre. 2008. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment 2008, 10 (Oct 2008), P10008

    2008

Show all 77 references
  1. [9]

    Paolo Boldi, Marco Rosa, Massimo Santini, and Sebastiano Vigna. 2011. Layered label propagation: A multiresolution coordinate-free ordering for compressing social networks. In Proceedings of the 20th international conference on World Wide Web. 587–596

    2011

  2. [10]

    Paolo Boldi and Sebastiano Vigna. 2004. The webgraph framework I: compression techniques. In Proceedings of the 13th international conference on World Wide Web. 595–602

    2004

  3. [11]

    Brandes, D

    U. Brandes, D. Delling, M. Gaertler, R. Gorke, M. Hoefer, Z. Nikoloski, and D. Wagner. 2007. On modularity clustering. IEEE transactions on knowledge and data engineering 20, 2 (2007), 172–188

    2007

  4. [12]

    Aaron Clauset, Mark EJ Newman, and Cristopher Moore. 2004. Finding commu- nity structure in very large networks. Physical review E 70, 6 (2004), 066111

    2004

  5. [13]

    Michele Coscia, Fosca Giannotti, and Dino Pedreschi. 2011. A classification for community discovery methods in complex networks. Statistical Analysis and Data Mining: The ASA Data Science Journal 4, 5 (2011), 512–546

    2011

  6. [14]

    Dipanjan Das and Slav Petrov. 2011. Unsupervised part-of-speech tagging with bilingual graph-based projections. InProceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies . 600–609

    2011

  7. [15]

    Jordi Duch and Alex Arenas. 2005. Community detection in complex networks using extremal optimization. Physical review E 72, 2 (2005), 027104

    2005

  8. [16]

    Imen Ben El Kouni, Wafa Karoui, and Lotfi Ben Romdhane. 2021. WLNI-LPA: Detecting Overlapping Communities in Attributed Networks based on Label Propagation Process.. In ICSOFT. 408–416. 8 𝜈-LPA: Fast GPU-based Label Propagation Algorithm (LPA) for Community Detection 0.01 0.1 ...

    2021

  9. [17]

    Golnoosh Farnadi, Zeinab Mahdavifar, Ivan Keller, Jacob Nelson, Ankur Teredesai, Marie-Francine Moens, and Martine De Cock. 2015. Scalable adaptive label propagation in Grappa. In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 1485–1491

    2015

  10. [18]

    Fortunato

    S. Fortunato. 2010. Community detection in graphs. Physics reports 486, 3-5 (2010), 75–174

    2010

  11. [19]

    Lars Gottesbüren, Tobias Heuer, Peter Sanders, and Sebastian Schlag. 2021. Scal- able Shared-Memory Hypergraph Partitioning. In 2021 Proceedings of the Work- shop on Algorithm Engineering and Experiments (ALENEX) . SIAM, 16–30

    2021

  12. [20]

    S. Gregory. 2010. Finding overlapping communities in networks by label propa- gation. New Journal of Physics 12 (10 2010), 103018. Issue 10

    2010

  13. [21]

    Roger Guimerà, DB Stouffer, Marta Sales-Pardo, EA Leicht, MEJ Newman, and Luis AN Amaral. 2010. Origin of compartmentalization in food webs. Ecology 91, 10 (2010), 2941–2951

    2010

  14. [22]

    Nandinee Haq and Z Jane Wang. 2016. Community detection from genomic datasets across human cancers. In 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 1147–1150

    2016

  15. [23]

    Yong He and Alan Evans. 2010. Graph theoretical modeling of brain connectivity. Current opinion in neurology 23, 4 (2010), 341–350

    2010

  16. [24]

    Vitali Henne. 2015. Label propagation for hypergraph partitioning . Ph. D. Disser- tation. Karlsruher Institut für Technologie (KIT)

    2015

  17. [25]

    S. Kang, C. Hastings, J. Eaton, and B. Rees. 2023. cuGraph C++ primitives: vertex/edge-centric building blocks for parallel graph computing. In IEEE Inter- national Parallel and Distributed Processing Symposium Workshops . 226–229

    2023

  18. [26]

    Pan-Jun Kim, Dong-Yup Lee, and Hawoong Jeong. 2009. Centralized modularity of N-linked glycosylation pathways in mammalian cells. PloS one 4, 10 (2009), e7317

    2009

  19. [27]

    Kloster and D

    K. Kloster and D. Gleich. 2014. Heat kernel based community detection. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining . ACM, New York, USA, 1386–1395

    2014

  20. [28]

    Kolodziej, M

    S. Kolodziej, M. Aznaveh, M. Bullock, J. David, T. Davis, M. Henderson, Y. Hu, and R. Sandstrom. 2019. The SuiteSparse matrix collection website interface. The Journal of Open Source Software 4, 35 (Mar 2019), 1244. 9 Subhajit Sahu

    2019

  21. [29]

    Yusuke Kozawa, Toshiyuki Amagasa, and Hiroyuki Kitagawa. 2017. Gpu- accelerated graph clustering via parallel label propagation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management . 567– 576

    2017

  22. [30]

    Rongrong Li, Wenzhong Guo, Kun Guo, and Qirong Qiu. 2015. Parallel multi- label propagation for overlapping community detection in large-scale networks. In Multi-disciplinary Trends in Artificial Intelligence: 9th International Workshop, MIW AI 2015, Fuzhou, China, November 13...

    2015

  23. [31]

    Jun Ma, Jenny Wang, Laleh Soltan Ghoraie, Xin Men, Benjamin Haibe-Kains, and Penggao Dai. 2019. A comparative study of cluster detection algorithms in protein–protein interaction for drug target discovery and drug repurposing. Frontiers in pharmacology 10 (2019), 109

    2019

  24. [32]

    Henning Meyerhenke, Peter Sanders, and Christian Schulz. 2014. Partitioning complex networks via size-constrained clustering. In International Symposium on Experimental Algorithms. Springer, 351–363

    2014

  25. [33]

    Henning Meyerhenke, Peter Sanders, and Christian Schulz. 2016. Partitioning (hierarchically clustered) complex networks via size-constrained graph clustering. Journal of Heuristics 22 (2016), 759–782

    2016

  26. [34]

    Henning Meyerhenke, Peter Sanders, and Christian Schulz. 2017. Parallel graph partitioning for complex networks. IEEE Transactions on Parallel and Distributed Systems 28, 9 (2017), 2625–2638

    2017

  27. [35]

    Anuraj Mohan, R Venkatesan, and KV Pramod. 2017. A scalable method for link prediction in large real world networks. J. Parallel and Distrib. Comput. 109 (2017), 89–101

    2017

  28. [36]

    M. Newman. 2006. Finding community structure in networks using the eigen- vectors of matrices. Physical review E 74, 3 (2006), 036104

    2006

  29. [37]

    John Nickolls and William J Dally. 2010. The GPU computing era. IEEE micro 30, 2 (2010), 56–69

    2010

  30. [38]

    Chengbin Peng, Tamara G Kolda, and Ali Pinar. 2014. Accelerating community detection by using k-core subgraphs. arXiv preprint arXiv:1403.2226 (2014)

    2014 arXiv

  31. [39]

    Ovidiu Popa, Einat Hazkani-Covo, Giddy Landan, William Martin, and Tal Dagan

  32. [40]

    Raghavan, R

    U. Raghavan, R. Albert, and S. Kumara. 2007. Near linear time algorithm to detect community structures in large-scale networks. Physical Review E 76, 3 (Sep 2007), 036106–1–036106–11

    2007

  33. [41]

    Jörg Reichardt and Stefan Bornholdt. 2006. Statistical mechanics of community detection. Physical review E 74, 1 (2006), 016110

    2006

  34. [42]

    Corban G Rivera, Rachit Vakil, and Joel S Bader. 2010. NeMo: network module identification in Cytoscape. BMC bioinformatics 11 (2010), 1–9

    2010

  35. [43]

    Hamid Roghani, Asgarali Bouyer, and Esmaeil Nourani. 2021. PLDLS: A novel parallel label diffusion and label Selection-based community detection algorithm based on Spark in social networks. Expert Systems with Applications 183 (2021), 115377

    2021

  36. [44]

    Rosvall and C

    M. Rosvall and C. Bergstrom. 2008. Maps of random walks on complex networks reveal community structure. Proceedings of the national academy of sciences 105, 4 (2008), 1118–1123

    2008

  37. [45]

    Subhajit Sahu. 2023. GVE-LPA: Fast Label Propagation Algorithm (LPA) for Community Detection in Shared Memory Setting.arXiv preprint arXiv:2312.08140 (2023)

    2023 arXiv

  38. [46]

    S. Sahu. 2023. Selecting a suitable Parallel Label-propagation based algorithm for Disjoint Community Detection. arXiv preprint arXiv:2301.09125 (2023)

    2023 arXiv

  39. [47]

    Marcel Salathé and James H Jones. 2010. Dynamics and control of diseases in networks with community structure. PLoS computational biology 6, 4 (2010), e1000736

    2010

  40. [48]

    Jason Sanders and Edward Kandrot. 2010. CUDA by example: an introduction to general-purpose GPU programming . Addison-Wesley Professional

    2010

  41. [49]

    Mohammad Sattari and Kamran Zamanifar. 2018. A spreading activation-based label propagation algorithm for overlapping community detection in dynamic social networks. Data & Knowledge Engineering 113 (2018), 155–170

    2018

  42. [50]

    Gui-Lan SHEN and Xiao-Ping YANG. 2016. A Topic Community Detection Method for Information Network based on Improved Label Propagation. Inter- national Journal of Hybrid Information Technology 9, 2 (2016), 299–310

    2016

  43. [51]

    George M Slota, Kamesh Madduri, and Sivasankaran Rajamanickam. 2014. PuLP: Scalable multi-objective multi-constraint partitioning for small-world networks. In 2014 IEEE International Conference on Big Data (Big Data) . IEEE, 481–490

    2014

  44. [52]

    George M Slota, Cameron Root, Karen Devine, Kamesh Madduri, and Sivasankaran Rajamanickam. 2020. Scalable, multi-constraint, complex-objective graph partitioning. IEEE Transactions on Parallel and Distributed Systems 31, 12 (2020), 2789–2801

    2020

  45. [53]

    Jyothish Soman and Ankur Narang. 2011. Fast community detection algorithm with gpus and multicore architectures. In 2011 IEEE International Parallel & Distributed Processing Symposium. IEEE, 568–579

    2011

  46. [54]

    Staudt, A

    C.L. Staudt, A. Sazonovs, and H. Meyerhenke. 2016. NetworKit: A tool suite for large-scale complex network analysis. Network Science 4, 4 (2016), 508–530

    2016

  47. [55]

    Stergios Stergiou, Dipen Rughwani, and Kostas Tsioutsiouliklis. 2018. Short- cutting label propagation for distributed connected components. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining . 540–546

    2018

  48. [56]

    Aaron M Tenenbaum. 1990. Data structures using C . Pearson Education India

    1990

  49. [57]

    Traag and L

    V.A. Traag and L. Šubelj. 2023. Large network community detection by fast label propagation. Scientific Reports 13, 1 (2023), 2701

    2023

  50. [58]

    Traag, L

    V. Traag, L. Waltman, and N. Eck. 2019. From Louvain to Leiden: guaranteeing well-connected communities. Scientific Reports 9, 1 (Mar 2019), 5233

    2019

  51. [59]

    Lucreţia Udrescu, Paul Bogdan, Aimée Chiş, Ioan Ovidiu Sîrbu, Alexandru Topîrceanu, Renata-Maria Văruţ, and Mihai Udrescu. 2020. Uncovering new drug properties in target-based drug–drug similarity networks. Pharmaceutics 12, 9 (2020), 879

    2020

  52. [60]

    Alan Valejo, Thiago Faleiros, Maria Cristina Ferreira de Oliveira, and Alneu de Andrade Lopes. 2020. A coarsening method for bipartite networks via weight- constrained label propagation. Knowledge-Based Systems 195 (2020), 105678

    2020

  53. [61]

    Lu Wang, Yanghua Xiao, Bin Shao, and Haixun Wang. 2014. How to partition a billion-node graph. In2014 IEEE 30th International Conference on Data Engineering. IEEE, 568–579

    2014

  54. [62]

    Yangzihao Wang, Andrew Davidson, Yuechao Pan, Yuduo Wu, Andy Riffel, and John D Owens. 2016. Gunrock: A high-performance graph processing library on the GPU. In Proceedings of the 21st ACM SIGPLAN symposium on principles and practice of parallel programming . 1–12

    2016

  55. [63]

    Yan Wang, Rongrong Ji, and Shih-Fu Chang. 2013. Label propagation from imagenet to 3d point clouds. In Proceedings of the IEEE conference on computer vision and pattern recognition . 3135–3142

    2013

  56. [64]

    Zehan Wang, Kanwal K Bhatia, Ben Glocker, Antonio Marvao, Tim Dawes, Kazu- nari Misawa, Kensaku Mori, and Daniel Rueckert. 2014. Geodesic patch-based segmentation. In Medical Image Computing and Computer-Assisted Intervention– MICCAI 2014: 17th International Conference, Boston...

    2014

  57. [65]

    Whang, D

    J. Whang, D. Gleich, and I. Dhillon. 2013. Overlapping community detection using seed set expansion. In Proceedings of the 22nd ACM international conference on Information & Knowledge Management . 2099–2108

    2013

  58. [66]

    Tianji Wu, Bo Wang, Yi Shan, Feng Yan, Yu Wang, and Ningyi Xu. 2010. Effi- cient pagerank and spmv computation on amd gpus. In 2010 39th International Conference on Parallel Processing . IEEE, 81–89

    2010

  59. [67]

    J. Xie, M. Chen, and B. Szymanski. 2013. LabelrankT: Incremental community detection in dynamic networks via label propagation. In Proceedings of the Work- shop on Dynamic Networks Management and Mining . ACM, New York, USA, 25–32

    2013

  60. [68]

    J. Xie, B. Szymanski, and X. Liu. 2011. SLPA: Uncovering overlapping communi- ties in social networks via a speaker-listener interaction dynamic process. InIEEE 11th International Conference on Data Mining Workshops . IEEE, IEEE, Vancouver, Canada, 344–349

    2011

  61. [69]

    Xiaolong Xu, Nan Hu, Tao Li, Marcello Trovati, Francesco Palmieri, Georgios Kontonatsios, and Aniello Castiglione. 2019. Distributed temporal link prediction algorithm based on label propagation. Future generation computer systems 93 (2019), 627–636

    2019

  62. [70]

    Chang Ye, Yuchen Li, Bingsheng He, Zhao Li, and Jianling Sun. 2023. Large-Scale Graph Label Propagation on GPUs. IEEE Transactions on Knowledge and Data Engineering (2023)

    2023

  63. [71]

    X. You, Y. Ma, and Z. Liu. 2020. A three-stage algorithm on community detection in social networks. Knowledge-Based Systems 187 (2020), 104822

    2020

  64. [72]

    Bagher Zarei, Mohammad Reza Meybodi, and Behrooz Masoumi. 2020. Detecting community structure in signed and unsigned social networks by using weighted label propagation. Chaos: An Interdisciplinary Journal of Nonlinear Science 30, 10 (2020)

    2020

  65. [73]

    Geng Zhang, Xinjie Gong, Yanan Wang, Yang Wang, and Hao Jiang. 2020. Mul- tilevel partition algorithm based on weighted label propagation. In 2020 IEEE International Conference on Smart Cloud (SmartCloud) . IEEE, 100–105

    2020

  66. [74]

    Weitong Zhang, Ronghua Shang, and Licheng Jiao. 2023. Large-scale community detection based on core node and layer-by-layer label propagation. Information Sciences 632 (2023), 1–18

    2023

  67. [75]

    Xian-Kun Zhang, Jing Ren, Chen Song, Jia Jia, and Qian Zhang. 2017. Label propagation algorithm for community detection based on node importance and label influence. Physics Letters A 381, 33 (2017), 2691–2698

    2017

  68. [76]

    Yu Zheng, Yongxin Zhu, Shijin Song, Peng Xiong, Zihao Cao, and Junjie Hou. 2018. Improved weighted label propagation algorithm in social network computing. In 17th IEEE TrustCom / 12th IEEE BigDataSE . IEEE, 1799–1803. 10

    2018

  69. [2011]

    Genome research 21, 4 (2011), 599–609

    Directed networks reveal genomic barriers and DNA repair bypasses to lateral gene transfer among prokaryotes. Genome research 21, 4 (2011), 599–609

    2011

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.