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Identifying vulnerable nodes for hypergraph dismantling via higher-order competition dynamics

T0 review · 2 major / 2 minor · reviewed 2026-06-26 · grok-4.3

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Pith's one-line read Hyper-VDrank identifies nodes whose removal collapses hypergraphs faster by modeling collective competition inside each hyperedge under strong deletion.

desk verdict hyper-VDrank targets strong-deletion dismantling with competition dynamics and vulnerability weights, but the reported gains rest on whether baselines were run under the same deletion rule. read the letter →

arxiv 2606.23289 v1 pith:6WJA3YJ4 submitted 2026-06-22 physics.soc-ph

Yajing Hao , Longzhao Liu , Xin Wang , Zhihao Han , Hongwei Zheng , Shaoting Tang This is my paper · ORCID
classification physics.soc-ph
keywords hypergraphdismantlingstrongdeletionhigher-ordernetworkscompetitiondynamicsvulnerabilityweightnodecentralitynetworkfragilitycomplexsystems
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

The paper develops hyper-VDrank to locate nodes that destroy connectivity in hypergraphs when entire group interactions fail upon one participant's removal. It replaces pairwise neighbor comparisons with a dynamics in which each node experiences pressure from all others sharing the same hyperedge and adds a weight that marks hyperedges as more or less vulnerable according to their redundancy and size. The resulting ranking sequence removes the largest connected component more quickly and drives the hypergraph to collapse at an earlier stage than methods built for pairwise networks or weak deletion. Tests across fourteen real hypergraphs produce average gains of 23.65 percent in dismantling efficiency and 27.63 percent in lowered collapse threshold.

What carries the argument

Higher-order competition dynamics mediated by hyperedge-induced environments, together with hyperedge vulnerability weight derived from redundancy and size effects.

What would settle it

Re-running the dismantling comparison on the same fourteen hypergraphs but under a weak-deletion rule where hyperedges merely shrink instead of collapsing would show whether the reported efficiency gains persist or disappear.

Watch

Extended reading notes

Core claim

Hyper-VDrank constructs a higher-order competition dynamics mediated by hyperedge-induced environments in which nodes respond to collective pressure formed by other nodes in the same hyperedge, then augments this with a hyperedge vulnerability weight based on redundancy and size effects; the combined measure distinguishes critical nodes for dismantling under the strong-deletion rule more effectively than classical or recent baselines.

Load-bearing premise

The strong-deletion rule, in which removal of one node causes the entire hyperedge to fail, correctly models the fragility processes in the fourteen real-world hypergraphs studied.

Editorial extensions

If this is right

  • The ranking removes the largest connected component more rapidly than existing methods.
  • The hypergraph reaches structural collapse at a lower fraction of nodes removed.
  • Greater fragmentation of the remaining structure occurs after each removal step.
  • Average dismantling efficiency across real hypergraphs rises by 23.65 percent.
  • The collapse threshold falls by 27.63 percent on average.

Reading between the lines

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

  • The same competition-plus-vulnerability logic could guide removal of key members in social or biological groups where one departure ends the whole interaction.
  • Testing the vulnerability weight on families of synthetic hypergraphs that vary only in redundancy would isolate how much that term contributes to the observed gains.
  • The approach supplies a concrete way to compare the fragility of higher-order versus pairwise representations of the same data.
  • Similar dynamics may extend to locating influential sets in higher-order spreading or synchronization processes.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 2 minor

Summary. The paper introduces hyper-VDrank, a centrality method for hypergraph dismantling under strong-deletion rules. It constructs higher-order competition dynamics in which nodes respond to collective hyperedge pressure and incorporates hyperedge vulnerability weights based on redundancy and size. Experiments on 14 real-world hypergraphs report that hyper-VDrank improves dismantling efficiency by 23.65% and lowers the collapse threshold by 27.63% on average relative to classical and recent baselines.

Significance. If the reported gains are shown to arise from the competition dynamics and vulnerability weights rather than from inconsistent application of the deletion rule, the work would supply a practically useful tool for identifying critical nodes in higher-order systems where group interactions fail entirely upon single-node removal. The framing of collective pressure mediated by hyperedges offers a distinct perspective from pairwise projections.

major comments (2)
  1. [Abstract] Abstract: the headline performance claims (23.65% efficiency gain, 27.63% threshold reduction) are load-bearing for the central contribution, yet the abstract and experimental description do not state whether the baseline methods were re-implemented under the strong-deletion rule or retained their original weak-deletion formulations. Because the manuscript explicitly contrasts the two rules, this omission prevents isolation of the contribution of the proposed dynamics and weights.
  2. [Experimental results] Experimental section (implied by the 14-hypergraph results): the free parameter governing hyperedge vulnerability weight scaling is listed among the method's adjustable quantities; the manuscript must report its value(s), selection procedure, and sensitivity analysis, as any data-driven tuning would undermine the claim that the reported averages reflect the intrinsic performance of the new construction.
minor comments (2)
  1. [Abstract] Abstract and methods: the averages over 14 hypergraphs are presented without error bars, standard deviations, or per-hypergraph breakdowns, making it impossible to assess whether the reported improvements are consistent or driven by a few outliers.
  2. [Data and methods] The manuscript should specify the exact criteria used to select the 14 real-world hypergraphs and any exclusion rules applied to the data.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. We address the major comments point by point below, providing clarifications and committing to revisions where appropriate to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the headline performance claims (23.65% efficiency gain, 27.63% threshold reduction) are load-bearing for the central contribution, yet the abstract and experimental description do not state whether the baseline methods were re-implemented under the strong-deletion rule or retained their original weak-deletion formulations. Because the manuscript explicitly contrasts the two rules, this omission prevents isolation of the contribution of the proposed dynamics and weights.

    Authors: We agree that explicit clarification is required to isolate the contribution of hyper-VDrank. In the original experiments, all baseline methods were re-implemented and evaluated under the strong-deletion rule to ensure consistency with the problem setting emphasized in the manuscript. The reported gains therefore reflect the advantage of the higher-order competition dynamics and vulnerability weights under this rule. In the revised manuscript, we will add an explicit statement in the Experimental Results section (and update the abstract if feasible) confirming that baselines were adapted to the strong-deletion setting. This addresses the concern directly. revision: yes

  2. Referee: [Experimental results] Experimental section (implied by the 14-hypergraph results): the free parameter governing hyperedge vulnerability weight scaling is listed among the method's adjustable quantities; the manuscript must report its value(s), selection procedure, and sensitivity analysis, as any data-driven tuning would undermine the claim that the reported averages reflect the intrinsic performance of the new construction.

    Authors: We acknowledge the need for full transparency on the hyperedge vulnerability weight scaling parameter. This parameter was fixed to a constant value chosen to reflect the relative importance of redundancy and size effects without per-dataset optimization. In the revision, we will report the specific value used in all experiments, describe the rationale and selection procedure (based on theoretical considerations of hyperedge properties rather than performance tuning), and include a sensitivity analysis demonstrating that the performance improvements remain consistent across a range of parameter values. This will confirm that the results are intrinsic to the method. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity; method is a new construction independent of its inputs

full rationale

The paper defines hyper-VDrank via explicit new components (higher-order competition dynamics mediated by hyperedge environments plus a vulnerability weight based on redundancy and size) that are not shown to reduce to fitted parameters or prior self-citations by construction. The abstract frames the approach as addressing a gap (strong deletion) with a distinct mechanism, and the reported efficiency gains are presented as outcomes of applying this construction rather than quantities defined via the same equations. No load-bearing step matches the enumerated circularity patterns.

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

The central claim rests on the domain assumption that strong deletion governs the studied systems and on a new weighting scheme whose construction details are not provided; no invented entities are introduced.

free parameters (1)
  • hyperedge vulnerability weight scaling
    Weights based on redundancy and size effects are introduced without explicit formulas or confirmation they are parameter-free.
assumptions (1)
  • domain assumption Strong deletion applies to many real higher-order systems
    Abstract states that failure of one participant may cause the entire group interaction to fail in many real systems.

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Cite this review

Pith. "Pith review of Identifying vulnerable nodes for hypergraph dismantling via higher-order competition dynamics." pith.science (2026). https://pith.science/paper/6WJA3YJ4

@misc{pith2026260623289,
  author       = {Pith},
  title        = {Pith review of: Identifying vulnerable nodes for hypergraph dismantling via higher-order competition dynamics},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6WJA3YJ4}},
  note         = {Machine review of arXiv:2606.23289}
}
read the original abstract

Network dismantling aims to identify a node removal sequence that can rapidly destroy network connectivity, which is an important problem for understanding the structural fragility of complex systems and designing intervention strategies. Existing studies mainly focus on pairwise networks or assume weak-deletion rules where node removal only causes hyperedges to shrink in higher-order networks. However, in many real higher-order systems, the failure of one participant may cause the entire group interaction to fail, i.e., the strong-deletion mechanism. Such a mechanism cannot be fully captured by projected networks or methods based on weak-deletion rules. To address this challenge, we propose hyper-Vulnerability-weighted Dominance rank (hyper-VDrank), a higher-order centrality method for hypergraph dismantling under strong deletion. Hyper-VDrank constructs a higher-order competition dynamics mediated by hyperedge-induced environments, where a node does not compete only with individual neighbors but responds to the collective pressure formed by other nodes in the same hyperedge. It further introduces a hyperedge vulnerability weight based on redundancy and size effects to capture the vulnerable structures, facilitating the distinction of critical nodes. Experiments show that hyper-VDrank reduces the largest connected component more rapidly, collapses the hypergraph earlier, and produces greater structural fragmentation than classical and recent methods. On 14 real-world hypergraphs, hyper-VDrank improves dismantling efficiency by 23.65% and reduces the collapse threshold by 27.63% on average compared with the baselines. In summary, hyper-VDrank offers an effective hypergraph dismantling tool and a new perspective on identifying vulnerable structures in higher-order complex systems.

Figures

Figures reproduced from arXiv: 2606.23289 by the authors.

Figure 1
Figure 1. Illustration of hypergraph representations and deletion mechanisms. (a) A toy hypergraph with 8 nodes [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Dismantling process of the 7 × 7 4-uniform regular hypergraph by hyper-VDrank (the upper row) and 2-betweenness (the lower row) under strong deletion. Node colors indicate the normalized measure scores assigned by the corresponding method, with darker colors representing higher scores and therefore earlier removal priority. Denote σ = α β > 0. The closed-form stationary solution is given by x ∗ = σθ(I + σM) −1k. (11… view at source ↗
Figure 3
Figure 3. Illustration of a hypergraph with community structure and its dismantling processes under different meth [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: LCC dismantling curves and parameter search process on synthetic hypergraphs. (a)–(c) LCC dismantling [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: LCC dismantling curves on real-world hypergraphs. Hyper-VDrank reduces the LCC and achieves [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]
Figure 6
Figure 6. Figure 6: Quantitative comparison of performance of various methods. (a) Normalized ANC difference of each [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]
Figure 7
Figure 7. Figure 7: CCS curves on real-world hypergraphs. A larger CCS indicates that the residual hypergraph is split into [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]

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Reference graph

Works this paper leans on

60 extracted references · 3 canonical work pages · cited by 1 Pith paper

  1. [1]

    The structure and function of complex networks.SIAM review, 45(2):167–256, 2003

    Mark EJ Newman. The structure and function of complex networks.SIAM review, 45(2):167–256, 2003

    2003

  2. [2]

    Complex networks: Structure and dynamics.Physics reports, 424(4-5):175–308, 2006

    Stefano Boccaletti, Vito Latora, Yamir Moreno, Martin Chavez, and D-U Hwang. Complex networks: Structure and dynamics.Physics reports, 424(4-5):175–308, 2006

    2006

  3. [3]

    Public discourse and social network echo chambers driven by socio-cognitive biases.Physical Review X, 10(4):041042, 2020

    Xin Wang, Antonio D Sirianni, Shaoting Tang, Zhiming Zheng, and Feng Fu. Public discourse and social network echo chambers driven by socio-cognitive biases.Physical Review X, 10(4):041042, 2020

    2020

  4. [4]

    Error and attack tolerance of complex networks

    R´ eka Albert, Hawoong Jeong, and Albert-L´ aszl´ o Barab´ asi. Error and attack tolerance of complex networks. Nature, 406(6794):378–382, 2000

    2000

  5. [5]

    Network dismantling.Pro- ceedings of the National Academy of Sciences, 113(44):12368–12373, 2016

    Alfredo Braunstein, Luca Dall’Asta, Guilhem Semerjian, and Lenka Zdeborov´ a. Network dismantling.Pro- ceedings of the National Academy of Sciences, 113(44):12368–12373, 2016

    2016

  6. [6]

    A comparative analysis of approaches to network-dismantling.Scientific reports, 8(1):13513, 2018

    Sebastian Wandelt, Xiaoqian Sun, Daozhong Feng, Massimiliano Zanin, and Shlomo Havlin. A comparative analysis of approaches to network-dismantling.Scientific reports, 8(1):13513, 2018

    2018

  7. [7]

    Recent advances in network dismantling: A comprehensive review and list of recommendations for future work.Chaos, Solitons & Fractals, 199:116673, 2025

    Sebastian Wandelt, Xinyue Chen, and Xiaoqian Sun. Recent advances in network dismantling: A comprehensive review and list of recommendations for future work.Chaos, Solitons & Fractals, 199:116673, 2025

    2025

  8. [8]

    Centrality in social networks conceptual clarification.Social networks, 1(3):215–239, 1978

    Linton C Freeman. Centrality in social networks conceptual clarification.Social networks, 1(3):215–239, 1978. 14

    1978

Show all 60 references
  1. [9]

    Power and centrality: A family of measures.American journal of sociology, 92(5):1170–1182, 1987

    Phillip Bonacich. Power and centrality: A family of measures.American journal of sociology, 92(5):1170–1182, 1987

    1987

  2. [10]

    Vital nodes identification in complex networks.Physics reports, 650:1–63, 2016

    Linyuan L¨ u, Duanbing Chen, Xiao-Long Ren, Qian-Ming Zhang, Yi-Cheng Zhang, and Tao Zhou. Vital nodes identification in complex networks.Physics reports, 650:1–63, 2016

    2016

  3. [11]

    Identification of influential spreaders in complex networks.Nature physics, 6(11):888–893, 2010

    Maksim Kitsak, Lazaros K Gallos, Shlomo Havlin, Fredrik Liljeros, Lev Muchnik, H Eugene Stanley, and Hern´ an A Makse. Identification of influential spreaders in complex networks.Nature physics, 6(11):888–893, 2010

    2010

  4. [12]

    Searching for superspreaders of information in real-world social media.Scientific reports, 4(1):5547, 2014

    Sen Pei, Lev Muchnik, Jos´ e S Andrade, Jr, Zhiming Zheng, and Hern´ an A Makse. Searching for superspreaders of information in real-world social media.Scientific reports, 4(1):5547, 2014

    2014

  5. [13]

    Local-forest method for superspreaders identification in online social networks.Entropy, 24(9):1279, 2022

    Yajing Hao, Shaoting Tang, Longzhao Liu, Hongwei Zheng, Xin Wang, and Zhiming Zheng. Local-forest method for superspreaders identification in online social networks.Entropy, 24(9):1279, 2022

    2022

  6. [14]

    Influence maximization in complex networks through optimal perco- lation.Nature, 524(7563):65–68, 2015

    Flaviano Morone and Hern´ an A Makse. Influence maximization in complex networks through optimal perco- lation.Nature, 524(7563):65–68, 2015

    2015

  7. [15]

    Immunization and targeted destruction of networks using explosive percolation.Physical review letters, 117(20):208301, 2016

    Pau Clusella, Peter Grassberger, Francisco J P´ erez-Reche, and Antonio Politi. Immunization and targeted destruction of networks using explosive percolation.Physical review letters, 117(20):208301, 2016

    2016

  8. [16]

    Domirank cen- trality reveals structural fragility of complex networks via node dominance.Nature communications, 15(1):56, 2024

    Marcus Engsig, Alejandro Tejedor, Yamir Moreno, Efi Foufoula-Georgiou, and Chaouki Kasmi. Domirank cen- trality reveals structural fragility of complex networks via node dominance.Nature communications, 15(1):56, 2024

    2024

  9. [17]

    Finding key players in complex networks through deep reinforcement learning.Nature machine intelligence, 2(6):317–324, 2020

    Changjun Fan, Li Zeng, Yizhou Sun, and Yang-Yu Liu. Finding key players in complex networks through deep reinforcement learning.Nature machine intelligence, 2(6):317–324, 2020

    2020

  10. [18]

    Machine learning dismantling and early-warning signals of disintegration in complex systems.Nature communications, 12(1):5190, 2021

    Marco Grassia, Manlio De Domenico, and Giuseppe Mangioni. Machine learning dismantling and early-warning signals of disintegration in complex systems.Nature communications, 12(1):5190, 2021

    2021

  11. [19]

    Encoding node diffusion competence and role significance for network dis- mantling

    Jiazheng Zhang and Bang Wang. Encoding node diffusion competence and role significance for network dis- mantling. InProceedings of the ACM Web Conference 2023, pages 111–121, 2023

    2023

  12. [20]

    The structure and dynamics of networks with higher order interactions.Physics Reports, 1018:1–64, 2023

    Stefano Boccaletti, Pietro De Lellis, Charo I del Genio, Karin Alfaro-Bittner, Regino Criado, Sarika Jalan, and Miguel Romance. The structure and dynamics of networks with higher order interactions.Physics Reports, 1018:1–64, 2023

    2023

  13. [21]

    Multistability, intermittency, and hybrid transitions in social contagion models on hypergraphs.Nature communications, 14(1):1375, 2023

    Guilherme Ferraz de Arruda, Giovanni Petri, Pablo Martin Rodriguez, and Yamir Moreno. Multistability, intermittency, and hybrid transitions in social contagion models on hypergraphs.Nature communications, 14(1):1375, 2023

    2023

  14. [22]

    Higher-order network adaptivity: co-evolution of higher-order structure and spreading dynamics.arXiv preprint arXiv:2508.15445, 2025

    Longzhao Liu, Hongwei Zheng, Zhihao Han, Xin Wang, and Shaoting Tang. Higher-order network adaptivity: co-evolution of higher-order structure and spreading dynamics.arXiv preprint arXiv:2508.15445, 2025

    2025

  15. [23]

    Simplicial models of social contagion.Nature communications, 10(1):2485, 2019

    Iacopo Iacopini, Giovanni Petri, Alain Barrat, and Vito Latora. Simplicial models of social contagion.Nature communications, 10(1):2485, 2019

    2019

  16. [24]

    Influential groups for seeding and sustaining nonlinear contagion in heterogeneous hyper- graphs.Communications Physics, 5(1):25, 2022

    Guillaume St-Onge, Iacopo Iacopini, Vito Latora, Alain Barrat, Giovanni Petri, Antoine Allard, and Laurent H´ ebert-Dufresne. Influential groups for seeding and sustaining nonlinear contagion in heterogeneous hyper- graphs.Communications Physics, 5(1):25, 2022

    2022

  17. [25]

    Connectivity in hypergraphs.Canadian Mathematical Bulletin, 61(2):252–271, 2018

    Megan Dewar, David Pike, and John Proos. Connectivity in hypergraphs.Canadian Mathematical Bulletin, 61(2):252–271, 2018

    2018

  18. [26]

    Higher-order interdependent percolation on hypergraphs

    Run-Ran Liu, Changchang Chu, and Fanyuan Meng. Higher-order interdependent percolation on hypergraphs. Chaos, Solitons & Fractals, 177:114246, 2023

    2023

  19. [27]

    Theory of percolation on hypergraphs.Physical Review E, 109(1):014306, 2024

    Ginestra Bianconi and Sergey N Dorogovtsev. Theory of percolation on hypergraphs.Physical Review E, 109(1):014306, 2024

    2024

  20. [28]

    Geometry of nonequilibrium reaction networks

    Sara Dal Cengio, Vivien Lecomte, and Matteo Polettini. Geometry of nonequilibrium reaction networks. Physical Review X, 13(2):021040, 2023. 15

    2023

  21. [29]

    Nature of hypergraph k-core percolation problems.Physical Review E, 109(1):014307, 2024

    Ginestra Bianconi and Sergey N Dorogovtsev. Nature of hypergraph k-core percolation problems.Physical Review E, 109(1):014307, 2024

    2024

  22. [30]

    Hyperci: a higher order collective influence measure for hypernetwork disman- tling

    Zijian Wu and Dengcheng Yan. Hyperci: a higher order collective influence measure for hypernetwork disman- tling. InSecond International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023), volume 12642, pages 401–406. SPIE, 2023

    2023

  23. [31]

    Influence maximization based on threshold models in hypergraphs.Chaos: An Interdisciplinary Journal of Nonlinear Science, 34(2), 2024

    Renquan Zhang, Xilong Qu, Qiang Zhang, Xirong Xu, and Sen Pei. Influence maximization based on threshold models in hypergraphs.Chaos: An Interdisciplinary Journal of Nonlinear Science, 34(2), 2024

    2024

  24. [32]

    Hypernetwork dismantling via deep reinforcement learning.IEEE Transactions on Network Science and Engineering, 9(5):3302–3315, 2022

    Dengcheng Yan, Wenxin Xie, Yiwen Zhang, Qiang He, and Yun Yang. Hypernetwork dismantling via deep reinforcement learning.IEEE Transactions on Network Science and Engineering, 9(5):3302–3315, 2022

    2022

  25. [33]

    Deep learning-based hypernetwork dismantling for effectively hindering structural recovery.Information Processing & Management, 63(3):104551, 2026

    Bin Tang, Hanqiang Deng, Yuxian Duan, Heng Zhang, Jian Huang, and Jiarui Zhang. Deep learning-based hypernetwork dismantling for effectively hindering structural recovery.Information Processing & Management, 63(3):104551, 2026

    2026

  26. [34]

    Betweenness approximation for edge computing with hypergraph neural networks.Tsinghua Science and Technology, 30(1):331–344, 2024

    Yaguang Guo, Wenxin Xie, Qingren Wang, Dengcheng Yan, and Yiwen Zhang. Betweenness approximation for edge computing with hypergraph neural networks.Tsinghua Science and Technology, 30(1):331–344, 2024

    2024

  27. [35]

    Hyper-cores promote localization and efficient seeding in higher-order processes.Nature communications, 14(1):6223, 2023

    Marco Mancastroppa, Iacopo Iacopini, Giovanni Petri, and Alain Barrat. Hyper-cores promote localization and efficient seeding in higher-order processes.Nature communications, 14(1):6223, 2023

    2023

  28. [36]

    Node and edge nonlinear eigenvector centrality for hypergraphs

    Francesco Tudisco and Desmond J Higham. Node and edge nonlinear eigenvector centrality for hypergraphs. Communications Physics, 4(1):201, 2021

    2021

  29. [37]

    Locating influential nodes in hypergraphs via fuzzy collective influence.Communications in Nonlinear Science and Numerical Simulation, 142:108574, 2025

    Su-Su Zhang, Xiaoyan Yu, Gui-Quan Sun, Chuang Liu, and Xiu-Xiu Zhan. Locating influential nodes in hypergraphs via fuzzy collective influence.Communications in Nonlinear Science and Numerical Simulation, 142:108574, 2025

    2025

  30. [38]

    Subhypergraphs in non-uniform random hypergraphs.arXiv preprint arXiv:1703.07686, 2017

    Megan Dewar, John Healy, Xavier P´ erez-Gim´ enez, Pawe l Pra lat, John Proos, Benjamin Reiniger, and Kirill Ternovsky. Subhypergraphs in non-uniform random hypergraphs.arXiv preprint arXiv:1703.07686, 2017

    2017 arXiv

  31. [39]

    Smallworldness in hypergraphs.Journal of Physics: Complexity, 4(3):035007, 2023

    Tanu Raghav, Stefano Boccaletti, and Sarika Jalan. Smallworldness in hypergraphs.Journal of Physics: Complexity, 4(3):035007, 2023

    2023

  32. [40]

    Xgi: A python package for higher-order interaction networks.Journal of Open Source Software, 8(85):5162, 2023

    Nicholas W Landry, Maxime Lucas, Iacopo Iacopini, Giovanni Petri, Alice Schwarze, Alice Patania, and Leo Torres. Xgi: A python package for higher-order interaction networks.Journal of Open Source Software, 8(85):5162, 2023

    2023

  33. [41]

    Hypergraphx: a library for higher- order network analysis.Journal of Complex Networks, 11(3):cnad019, 2023

    Quintino Francesco Lotito, Martina Contisciani, Caterina De Bacco, Leonardo Di Gaetano, Luca Gallo, Alberto Montresor, Federico Musciotto, Nicolo Ruggeri, and Federico Battiston. Hypergraphx: a library for higher- order network analysis.Journal of Complex Networks, 11(3):cnad019, 2023

    2023

  34. [42]

    Prob- abilistic activity driven model of temporal simplicial networks and its application on higher-order dynamics

    Zhihao Han, Longzhao Liu, Xin Wang, Yajing Hao, Hongwei Zheng, Shaoting Tang, and Zhiming Zheng. Prob- abilistic activity driven model of temporal simplicial networks and its application on higher-order dynamics. Chaos: An Interdisciplinary Journal of Nonlinear Science, 34(2), 2024

    2024

  35. [43]

    Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys.PloS one, 10(9):e0136497, 2015

    Rossana Mastrandrea, Julie Fournet, and Alain Barrat. Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys.PloS one, 10(9):e0136497, 2015

    2015

  36. [44]

    Can co-location be used as a proxy for face-to-face contacts?EPJ Data Science, 7(1):1–18, 2018

    Mathieu G´ enois and Alain Barrat. Can co-location be used as a proxy for face-to-face contacts?EPJ Data Science, 7(1):1–18, 2018

    2018

  37. [45]

    High-resolution measurements of face-to-face contact patterns in a primary school.PloS one, 6(8):e23176, 2011

    Juliette Stehl´ e, Nicolas Voirin, Alain Barrat, Ciro Cattuto, Lorenzo Isella, Jean-Fran¸ cois Pinton, Marco Quag- giotto, Wouter Van den Broeck, Corinne R´ egis, Bruno Lina, et al. High-resolution measurements of face-to-face contact patterns in a primary school.PloS one, 6(8...

    2011

  38. [46]

    Simplicial closure and higher-order link prediction.Proceedings of the National Academy of Sciences, 115(48):E11221–E11230, 2018

    Austin R Benson, Rediet Abebe, Michael T Schaub, Ali Jadbabaie, and Jon Kleinberg. Simplicial closure and higher-order link prediction.Proceedings of the National Academy of Sciences, 115(48):E11221–E11230, 2018

    2018

  39. [47]

    Estimating potential infection transmission routes in hospital wards using wearable proximity sensors.PloS one, 8(9):e73970, 2013

    Philippe Vanhems, Alain Barrat, Ciro Cattuto, Jean-Fran¸ cois Pinton, Nagham Khanafer, Corinne R´ egis, Byeul- a Kim, Brigitte Comte, and Nicolas Voirin. Estimating potential infection transmission routes in hospital wards using wearable proximity sensors.PloS one, 8(9):e73970...

    2013

  40. [48]

    Data on face-to-face contacts in an office building suggest a low-cost vaccination strategy based on community linkers.Network Science, 3(3):326–347, 2015

    Mathieu G´ enois, Christian L Vestergaard, Julie Fournet, Andr´ e Panisson, Isabelle Bonmarin, and Alain Barrat. Data on face-to-face contacts in an office building suggest a low-cost vaccination strategy based on community linkers.Network Science, 3(3):326–347, 2015

    2015

  41. [49]

    The role of heterogeneity in contact timing and duration in network models of influenza spread in schools.Journal of The Royal Society Interface, 12(108):20150279, 2015

    Damon JA Toth, Molly Leecaster, Warren BP Pettey, Adi V Gundlapalli, Hongjiang Gao, Jeanette J Rainey, Amra Uzicanin, and Matthew H Samore. The role of heterogeneity in contact timing and duration in network models of influenza spread in schools.Journal of The Royal Society In...

    2015

  42. [50]

    Motifs in temporal networks

    Ashwin Paranjape, Austin R Benson, and Jure Leskovec. Motifs in temporal networks. InProceedings of the tenth ACM international conference on web search and data mining, pages 601–610, 2017

    2017

  43. [51]

    The enron corpus: A new dataset for email classification research

    Bryan Klimt and Yiming Yang. The enron corpus: A new dataset for email classification research. InEuropean conference on machine learning, pages 217–226. Springer, 2004

    2004

  44. [52]

    A network analysis of commit- tees in the US House of Representatives.Proceedings of the National Academy of Sciences, 102(20):7057–7062, 2005

    Mason A Porter, Peter J Mucha, Mark EJ Newman, and Casey M Warmbrand. A network analysis of commit- tees in the US House of Representatives.Proceedings of the National Academy of Sciences, 102(20):7057–7062, 2005

    2005

  45. [53]

    Legislative cosponsorship networks in the US House and Senate.Social networks, 28(4):454– 465, 2006

    James H Fowler. Legislative cosponsorship networks in the US House and Senate.Social networks, 28(4):454– 465, 2006

    2006

  46. [54]

    Generative hypergraph clustering: From blockmodels to modularity.Science Advances, 7(28):eabh1303, 2021

    Philip S Chodrow, Nate Veldt, and Austin R Benson. Generative hypergraph clustering: From blockmodels to modularity.Science Advances, 7(28):eabh1303, 2021

    2021

  47. [55]

    Justifying recommendations using distantly-labeled reviews and fine-grained aspects

    Jianmo Ni, Jiacheng Li, and Julian McAuley. Justifying recommendations using distantly-labeled reviews and fine-grained aspects. InProceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural langu...

    2019

  48. [56]

    Diverse and experienced group discovery via hypergraph clustering

    Ilya Amburg, Nate Veldt, and Austin R Benson. Diverse and experienced group discovery via hypergraph clustering. InProceedings of the 2022 SIAM International Conference on Data Mining (SDM), pages 145–153. SIAM, 2022

    2022

  49. [57]

    Generalized network dismantling

    Xiao-Long Ren, Niels Gleinig, Dirk Helbing, and Nino Antulov-Fantulin. Generalized network dismantling. Proceedings of the national academy of sciences, 116(14):6554–6559, 2019

    2019

  50. [58]

    Effective approach to epidemic containment using link equations in complex networks.Science advances, 4(12):eaau4212, 2018

    Joan T Matamalas, Alex Arenas, and Sergio G´ omez. Effective approach to epidemic containment using link equations in complex networks.Science advances, 4(12):eaau4212, 2018

    2018

  51. [59]

    Effective epidemic containment strategy in hypergraphs.Physical Review Research, 3(3):033282, 2021

    Bukyoung Jhun. Effective epidemic containment strategy in hypergraphs.Physical Review Research, 3(3):033282, 2021

    2021

  52. [60]

    A unified framework for identifying influential nodes in hypergraphs.arXiv preprint arXiv:2512.09606, 2025

    Yajing Hao, Longzhao Liu, Xin Wang, Zhihao Han, Ming Wei, Zhiming Zheng, and Shaoting Tang. A unified framework for identifying influential nodes in hypergraphs.arXiv preprint arXiv:2512.09606, 2025. 17 Appendix A Search for the optimal competition parameter in real- world hyp...

    2025

Pith tools

Reviewed June 26, 2026 · model on record in the stance chip above.