Adaptive higher-order contagion of harmful information with platform-induced group dissolution and individual rewiring

Chunyu Luo; Hongwei Zheng; Longzhao Liu; Shaoting Tang; Xingru Chen; Xin Wang; Zhihao Han

REVIEW 3 major objections 5 minor 49 references

Adaptive higher-order contagion of harmful information with platform-induced group dissolution and individual rewiring

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read In adaptive higher-order networks, platform group dissolution only curbs harmful information within a bounded infection-rate window; outside it, more dissolution amplifies the spread.

desk verdict A clean modeling paper that finds a new backfiring window for group dissolution in adaptive higher-order contagion; the effect holds within the model but leans on one untested rewiring rule. read the letter →

arxiv 2608.07874 v1 pith:FD3KWJGO submitted 2026-08-08 physics.soc-ph

Longzhao Liu , Zhihao Han , Xingru Chen , Chunyu Luo , Hongwei Zheng , Xin Wang , Shaoting Tang This is my paper · ORCID

classification physics.soc-ph MSC 05C8291D30 PACS 89.65.-s89.75.-k

keywords harmfulinformationhigher-ordercontagionadaptivehypergraphsgroupdissolutionrewiringhomophilyphasetransitionplatformintervention

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 asks whether a platform that dissolves groups sharing harmful information can reduce the spread when users adapt by rewiring into new groups. It builds an adaptive higher-order contagion model on hypergraphs, where an ignorant individual in a group with k spreaders becomes infected at rate βk^v, groups dissolve at rate r·h^(k−1), and a spreader from a broken group recruits m−1 others into a fresh m-hyperedge. The central result is that dissolution helps only if the infection rate stays below a critical threshold; above that threshold, more dissolution increases prevalence. Within the effective window, dissolution first raises prevalence and then, past a critical dissolution rate, causes the infection to vanish through a discontinuous transition. The paper also shows that stronger higher-order reinforcement widens the window, while homophilic rewiring narrows it.

What carries the argument

The analysis is carried by a hyperedge-based mean-field approximation: the contagion is tracked by the variables L_{m,k}, the number of m-hyperedges containing k spreaders, with the spreader count N_S evolving according to dN_S/dt = −μN_S + β ∑_m ∑_{k=1}^m (m−k) k^v L_{m,k}; a companion ODE for dL_{m,k}/dt incorporates infection, recovery, dissolution at rate π_k = r·h^(k−1), and rewiring that re-forms an m-hyperedge around a spreader chosen with homophily λ. Linearization at the spreader-free equilibrium yields a closed-form invasion threshold β_c from a 3×3 determinant condition. The discontinuous-collapse and backfiring results follow from the bifurcation structure of these ODEs, validated by Gillespie simulations.

What would settle it

Run the same model with a rewiring rule in which a dissolved group's members do not reform around a spreader—for instance, they join existing groups chosen uniformly, or leave the platform with some probability. If increasing the dissolution rate then never increases prevalence across any infection rate, the backfiring window is an artifact of the respawning-spreader assumption rather than a generic property of adaptive rewiring.

Watch

Extended reading notes

Core claim

The paper shows that platform-induced group dissolution and individual adaptive rewiring jointly produce a non-monotonic and sometimes counterproductive effect of intervention. In a hypergraph where an ignorant in a group of k spreaders becomes infected at rate βk^v, groups dissolve at rate r·h^(k−1), and dissolved spreaders re-form m-sized groups, there exists a critical infection rate β_c. Below β_c, increasing the dissolution rate r initially enhances prevalence until r exceeds a threshold r_c, at which the system undergoes a discontinuous transition to the spreader-free state. Above β_c, prevalence grows monotonically with r and cannot be eradicated even as r tends to infinity, so dissolution backfires. Higher-order reinforcement h expands the β-window, whereas rewiring homophily λ shrinks it and raises r_c. These findings hold in both synthetic 3-uniform hypergraphs and an empirical high-school contact hypergraph.

Load-bearing premise

The result relies on the rule that whenever a platform dissolves a group, a spreader from that group immediately creates a new same-sized group by recruiting other users, so no spreader ever leaves the platform and the total number of groups stays constant.

Editorial extensions

If this is right

Dissolving groups is a reliable intervention only for low infection rates; above the critical rate, platforms that dissolve more groups will unintentionally increase the fraction of spreaders.
Below the critical infection rate, a moderate increase in dissolution can transiently worsen an outbreak, so the intervention must cross a threshold r_c to be useful.
Strengthening higher-order reinforcement (larger h) lowers the dissolution rate needed to eradicate contagion and extends the range of infection rates over which dissolution works.
Rewiring homophily (spreaders preferentially regrouping with spreaders) raises the eradication threshold and shrinks the effective window, making the intervention less robust.
The qualitative pattern holds on an empirical high-school contact hypergraph, not only on synthetic 3-uniform hypergraphs.

Reading between the lines

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

If the backfiring mechanism is driven by the guaranteed re-formation of groups around spreaders, then interventions that prevent re-formation—such as banning the organizer or rate-limiting new group creation—should convert the backfiring regime into an effective one; this is a testable distinction between the model and real platform policies.
The same bistability logic suggests that β_c marks a fold point of the prevalence landscape; measuring the bifurcation diagram on a real platform by varying enforcement intensity and observing prevalence jumps would directly test the predicted discontinuous eradication.
The model implies that a platform's optimal intervention is not maximal enforcement but a dose just above r_c within the effective window; enforcement above the window's boundary is actively harmful, which could inform graduated-response policies.
Rewiring homophily narrowing the window suggests that backfire is worsened when dissolved users cluster ideologically; combining dissolution with measures that reduce homophily should widen the effective window even without changing the infection rate.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Should know: this is a clean modeling paper, not a measurement paper. The new thing is a mean-field theory for coevolving group dissolution and spreader-led rewiring in higher-order contagion, and a phase diagram with a vertical asymptote in the r_c-vs-beta plane. The result: dissolution helps only below a critical infection rate; above it, dissolution backfires. The mean-field equations agree with Gillespie simulations on synthetic and empirical hypergraphs for the parameter sets shown, and code and data are available. That is reproducible evidence.

What is genuinely new: earlier work treated static hyperedge removal or adaptive rewiring separately; this paper puts both together and finds non-obvious behavior—the effective window, the non-monotonic response inside it, and the narrowing of the window under rewiring homophily. The invasion threshold in Eq. (7) is derived via linearization (Appendix A), which is solid. The citation pattern looks appropriate: the paper builds on the standard higher-order contagion and deplatforming literatures, and the self-citations are relevant rather than padding.

Soft spots, in decreasing order of importance. First, the rewiring rule is load-bearing and untested. Section 2.1 assumes every dissolved m-hyperedge is instantly replaced by a new one led by a spreader, keeping E_m constant. Because replacement is biased toward spreaders, dissolution reshuffles transmission channels rather than deleting them—that is what creates the backfiring. If dissolved groups were not replaced, or if regrouping were random, the effect could weaken or vanish. The conclusion mentions 'diverse adaptive rewiring rules' as future work, but the abstract and title present the result as general. Robustness checks against no-replacement and random-replacement rules should be in the paper, not the sequel.

Second, the critical infection rate bounding the window is observed as a vertical asymptote in numerical r_c curves; there is no analytic argument for the asymptote's location. That is acceptable for a physics paper, but it should be stated as a numerical finding. Third, the simulations are run 20 times without error bars, so the abrupt-collapse curves need confidence intervals. Fourth, there are production artifacts: Chinese text left in the Fig. 2 and Fig. 6 captions. These are minor but should be fixed.

One thing the reader's report calls 'circularity' is not really a flaw: the model is validated against simulations of the same rules, and the invasion threshold is derived from the same ODEs. That is internal consistency, not fitting to external data. For this type of theoretical paper it is fine, as long as nobody reads 'simulations on empirical hypergraph' as an empirical test of deplatforming policy.

Who benefits: researchers working on interventions in adaptive higher-order networks, and anyone citing static-hyperedge-removal results for policy. The central claim holds within the model; the main weakness is one strong assumption plus missing robustness analysis. I would send this to a serious referee; with a robustness section and cleanup it is publishable.

Referee Report

3 major / 5 minor

Summary. This paper studies a coevolving hypergraph model of harmful-information contagion in which platforms dissolve groups (hyperedges) containing spreaders while users rewire dissolved groups into new hyperedges. After presenting the microscopic rules (§2.1), the authors derive closed mean-field equations for the number of spreaders and the counts of m-hyperedges with k spreaders (Eqs. 5–6) using a hyperedge-based approximation, and compute the invasion threshold for 3-uniform hypergraphs (Eq. 7 and Appendix A). On synthetic hypergraphs, numerical solutions reproduce Gillespie simulations and reveal that increasing the dissolution rate can either eradicate contagion through a discontinuous transition below a critical infection rate, or backfire and increase prevalence above it. The paper further reports that larger higher-order reinforcement h expands this effective infection-rate window, while stronger rewiring homophily λ narrows it. A simulation study on an empirical high-school contact hypergraph is presented as qualitative validation.

Significance. If the main claim holds, the paper's message is practically important: platform-induced group dissolution is not monotonically beneficial once user adaptation is included, and there is a bounded infection-rate regime in which it works. The mean-field approximation is carefully set up, and the agreement between Eqs. (5)–(6) and Gillespie simulations for the reported parameter sets is a genuine strength, as is the provision of code and data. However, the headline 'effective window' is currently a numerical observation contingent on a specific rewiring mechanism, and the empirical-hypergraph section is only qualitative. With additional robustness analysis the paper would be a solid contribution.

major comments (3)

[§2.1, Eq. (6)] The central modeling assumption is the rewiring rule introduced in §2.1: whenever an m-hyperedge is dissolved, a spreader from the dissolved group immediately creates a new m-hyperedge by sampling m−1 nodes according to p_S in Eq. (3). This keeps E_m constant in Eq. (6) and, for λ>1, systematically biases replacement hyperedges toward spreader-rich configurations. Since the backfiring window is a consequence of this state-dependent replacement (dissolution reshuffles transmission channels rather than removing them), the result cannot be claimed for adaptive behavior in general without testing alternatives. I request at least three robustness checks: (i) no replacement (E_m decreases upon dissolution), (ii) a randomly chosen member of the dissolved group leads the new hyperedge, and (iii) new hyperedges are formed by nodes sampled uniformly at random (p_S = N_S/N). If the qualitative window persists under these variants, the claim is much stronger; if it disappears, the paper should explicitly frame the result as conditional on the spreader-led replacement rule.
[§3.1, Figs. 2(d) and 4(c)] The effective window is defined by vertical asymptotes in the r_c–β plane, but these asymptotes are obtained only by numerical continuation of r_c over a finite range of r. I could not find an analytic derivation of the critical infection rate (the 'critical infection rate' in the abstract) or a proof that for β above the asymptote no finite (or infinite) dissolution rate can eradicate contagion. Because this is the central qualitative claim, the authors should either derive the boundary from the fixed-point structure of Eqs. (5)–(6) (for example, by analyzing the r→∞ limit) or provide systematic large-r simulations that demonstrate the asymptote and its parameter dependence. Without this, the statement that 'even the limit r→∞ fails' in §3.1 is an extrapolation.
[§3.2, Fig. 6] The empirical-hypergraph section reports only simulation results; no theoretical curves from Eqs. (5)–(6) are shown for this mixed 2- and 3-uniform hypergraph, and the text does not specify how the rewiring step is implemented when hyperedges of different sizes coexist or whether E_m is conserved separately for m=2 and m=3. To support the statement that 'these results on empirical hypergraph confirm the robustness of our findings,' the authors should state the simulation protocol explicitly and, ideally, compare the observed prevalence curves with the hyperedge-approximation equations extended to the empirical structure.

minor comments (5)

[Fig. 2 and Fig. 6 captions] The captions contain untranslated Chinese text ('统一参数...'), which should be removed or translated into English.
[§2.2, Eq. (4)] Please clarify that Θ(t) treats each incident hyperedge independently and state this as part of the closure approximation; as written, an ignorant's infection rate is the sum over all incident hyperedges, which is an independence assumption rather than an exact consequence of the model.
[§3.1] The parameters are only listed in figure captions; a table of default parameters would improve reproducibility.
[§2.1, Eq. (2)] The phrase 'without loss of generality' is too strong for the choice π_k = r h^{k−1}; please rephrase as a minimal instantiation of the two stated conditions.
[§3.1, Fig. 2(d)] Define r_c precisely and describe how it is extracted from simulations or numerical solutions, since the vertical asymptote is central to the paper's message.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the effective-window and backfiring results are direct consequences of the explicitly stated ODEs and are cross-checked by stochastic simulations of the same microscopic rules.

full rationale

The derivation chain is self-contained. Section 2.1 states the microscopic rules: infection kernel theta_k = beta k^v, dissolution rate pi_k = r h^(k-1), and rewiring probability p_S = lambda N_S / (lambda N_S + N_I). Section 2.2 converts these rules into the closed ODE system (5)-(6) with explicit structural gain and loss terms. The invasion threshold (7) is obtained by linearizing these ODEs in Appendix A, and the effective-window/backfiring results are obtained by numerical analysis of the same system, not by fitting parameters or by importing a prior result. The Gillespie simulations implement the same microscopic rules, so the agreement in Figs. 2-6 validates the mean-field approximation rather than providing an external prediction. The only self-citation of note is Ref. [38], which motivates the hyperedge-based approximation ('Inspired by recent works [37, 38]'); this citation is not load-bearing because the ODEs are written out and validated in the present paper. The functional form pi_k = r h^(k-1) is a stated modeling choice, not a parameter fitted to the plotted prevalence curves. No step in the derivation chain reduces to its own input by construction.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The model introduces no new physical entities. It relies on a set of modeling assumptions: a mean-field closure, a specific rewiring rule led by spreaders, an ad hoc dissolution rate function, conservation of hyperedge count, and a standard linear-stability analysis for the invasion threshold. The free parameters (v, μ, ρ0) are chosen by hand but are not fitted to any target result.

free parameters (3)

v (infection reinforcement exponent) = 2.8 (synthetic), 3.0 (real)
Sets the nonlinearity of the infection kernel θ_k = β k^v; chosen from typical values in higher-order contagion literature, not fitted to data.
μ (recovery rate) = 0.5
Rate at which spreaders become ignorant; fixed by hand in all simulations.
ρ0 (initial spreader density) = 0.01 and 0.7
Initial conditions used to map bistability; chosen as low and high density cases.

assumptions (5)

domain assumption Mean-field independence: the infection rate of an ignorant individual is the global average Θ(t) over all incident hyperedges (Eq. 4), ignoring state correlations across hyperedges.
This closure makes Eqs. (5)-(6) a closed autonomous system; validated only indirectly against simulations.
domain assumption Rewiring by spreader: a dissolved hyperedge is replaced by a new hyperedge formed by one of its spreaders sampling m-1 nodes with probability p_S defined by global fractions (Eq. 3).
This is the mechanism that produces the backfiring window; alternative rewiring rules are not tested.
ad hoc to paper Dissolution rate function π_k = r h^{k-1} (Eq. 2) is a monotone instantiation of f(k;r,h) with f(1)=r.
The paper calls this 'without loss of generality', but qualitative results may depend on the function's curvature.
domain assumption Hyperedge count is conserved: every broken hyperedge is immediately replaced, preventing fragmentation.
Stated 'to prevent network fragmentation and ensure analytical tractability'; real deplatforming may permanently remove groups.
standard math Invasion threshold from linear stability: loss of stability of the spreader-free equilibrium occurs when det A_3(β)=0 (Appendix A).
Standard linear stability analysis for a Metzler Jacobian.

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Cite this review

Pith. "Pith review of Adaptive higher-order contagion of harmful information with platform-induced group dissolution and individual rewiring." pith.science (2026). https://pith.science/paper/FD3KWJGO

@misc{pith2026260807874,
  author       = {Pith},
  title        = {Pith review of: Adaptive higher-order contagion of harmful information with platform-induced group dissolution and individual rewiring},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FD3KWJGO}},
  note         = {Machine review of arXiv:2608.07874}
}

read the original abstract

Curbing harmful information contagion remains a critical challenge, motivating platform-level interventions such as group dissolution to sever transmission chains. However, in practice, users affected by dissolution often exhibit adaptive behavior, rewiring to form new groups. Yet, it remains unclear how these two mechanisms jointly shape information contagion and whether group dissolution remains effective in suppressing it. Here, we develop an adaptive higher-order contagion model that integrates platform-induced group dissolution with user adaptive rewiring, and derive a theoretical framework. Notably, we reveal an effective window for group dissolution, bounded by a critical infection rate. Above this threshold, dissolution backfires and amplifies information prevalence. Within this window, dissolution acts non-monotonically, initially exacerbating prevalence before eradicating contagion via a discontinuous transition beyond a critical dissolution rate. We further show that higher-order reinforcement expands this infection-rate window over which dissolution remains effective, whereas rewiring homophily substantially narrows it. Simulations on empirical hypergraph also validate these findings. Our work highlights the interplay between top-down platform interventions and bottom-up user adaptation, underscoring the need to account for adaptive responses when designing strategies to curb harmful information without unintended amplification.

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Reference graph

Works this paper leans on

49 extracted references · 47 canonical work pages

[1]

Science359(6380), 1094–1096 (2018)

Lazer, D.M., Baum, M.A., Benkler, Y., Berinsky, A.J., Greenhill, K.M., Menczer, F., Metzger, M.J., Nyhan, B., Pennycook, G., Rothschild, D.,et al.: The science of fake news. Science359(6380), 1094–1096 (2018)

work page 2018
[2]

Nature human behaviour5(3), 337–348 (2021)

Loomba, S., De Figueiredo, A., Piatek, S.J., De Graaf, K., Larson, H.J.: Measuring the impact of covid-19 vaccine misinformation on vaccination intent in the uk and usa. Nature human behaviour5(3), 337–348 (2021)

work page 2021
[3]

Journal of the European Economic Association19(4), 2131–2167 (2021)

M¨ uller, K., Schwarz, C.: Fanning the flames of hate: Social media and hate crime. Journal of the European Economic Association19(4), 2131–2167 (2021)

work page 2021
[4]

Science352(6292), 1459–1463 (2016) 13

Johnson, N.F., Zheng, M., Vorobyeva, Y., Gabriel, A., Qi, H., Vel´ asquez, N., Manrique, P., Johnson, D., Restrepo, E., Song, C.,et al.: New online ecology of adversarial aggregates: Isis and beyond. Science352(6292), 1459–1463 (2016) 13

work page 2016
[5]

Proceedings of the national academy of Sciences113(3), 554–559 (2016)

Del Vicario, M., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., Stanley, H.E., Quattrociocchi, W.: The spreading of misinformation online. Proceedings of the national academy of Sciences113(3), 554–559 (2016)

work page 2016
[6]

science 359(6380), 1146–1151 (2018)

Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018)

2018
[7]

Proceedings of the national academy of sciences118(9), 2023301118 (2021)

Cinelli, M., De Francisci Morales, G., Galeazzi, A., Quattrociocchi, W., Starnini, M.: The echo chamber effect on social media. Proceedings of the national academy of sciences118(9), 2023301118 (2021)

work page 2021
[8]

Physical Review X10(4), 041042 (2020)

Wang, X., Sirianni, A.D., Tang, S., Zheng, Z., Fu, F.: Public discourse and social network echo chambers driven by socio-cognitive biases. Physical Review X10(4), 041042 (2020)

work page 2020

Show all 49 references

[9]

Proceedings of the ACM on human-computer interaction 1(CSCW), 1–22 (2017)

Chandrasekharan, E., Pavalanathan, U., Srinivasan, A., Glynn, A., Eisenstein, J., Gilbert, E.: You can’t stay here: The efficacy of reddit’s 2015 ban examined through hate speech. Proceedings of the ACM on human-computer interaction 1(CSCW), 1–22 (2017)

2017
[10]

Nature 630(8015), 132–140 (2024)

McCabe, S.D., Ferrari, D., Green, J., Lazer, D.M., Esterling, K.M.: Post- january 6th deplatforming reduced the reach of misinformation on twitter. Nature 630(8015), 132–140 (2024)

2024
[11]

Nature573(7773), 261–265 (2019)

Johnson, N.F., Leahy, R., Restrepo, N.J., Vel´ asquez, N., Zheng, M., Manrique, P., Devkota, P., Wuchty, S.: Hidden resilience and adaptive dynamics of the global online hate ecology. Nature573(7773), 261–265 (2019)

2019
[12]

Proceedings of the National Academy of Sciences120(24), 2214080120 (2023)

Thomas, D.R., Wahedi, L.A.: Disrupting hate: The effect of deplatforming hate organizations on their online audience. Proceedings of the National Academy of Sciences120(24), 2214080120 (2023)

2023
[13]

PNAS nexus2(11), 346 (2023)

Mekacher, A., Falkenberg, M., Baronchelli, A., Rand, D.,et al.: The systemic impact of deplatforming on social media. PNAS nexus2(11), 346 (2023)

2023
[14]

REVIEWS OF MODERN PHYSICS Rev Mod Phys87(3), 925 (2015)

Pastor-Satorras, R., Castellano, C., Van Mieghem, P., Vespignani, A.: Epidemic processes in complex networks. REVIEWS OF MODERN PHYSICS Rev Mod Phys87(3), 925 (2015)

2015
[15]

Science advances4(12), 4212 (2018)

Matamalas, J.T., Arenas, A., G´ omez, S.: Effective approach to epidemic contain- ment using link equations in complex networks. Science advances4(12), 4212 (2018)

2018
[16]

arXiv preprint arXiv:2607.10171 (2026)

Han, Z., Liu, L., Wang, X., Hao, Y., Zheng, H., Tang, S.: Immunization on temporal higher-order networks. arXiv preprint arXiv:2607.10171 (2026)

2026 arXiv
[17]

14 Nature communications10(1), 2485 (2019)

Iacopini, I., Petri, G., Barrat, A., Latora, V.: Simplicial models of social contagion. 14 Nature communications10(1), 2485 (2019)

2019
[18]

Physical Review Research2(2), 023032 (2020)

Arruda, G.F., Petri, G., Moreno, Y.: Social contagion models on hypergraphs. Physical Review Research2(2), 023032 (2020)

2020
[19]

Nature communications14(1), 1375 (2023)

Arruda, G., Petri, G., Rodriguez, P.M., Moreno, Y.: Multistability, intermit- tency, and hybrid transitions in social contagion models on hypergraphs. Nature communications14(1), 1375 (2023)

2023
[20]

Nature Reviews Physics6(8), 468–482 (2024)

Arruda, G., Aleta, A., Moreno, Y.: Contagion dynamics on higher-order networks. Nature Reviews Physics6(8), 468–482 (2024)

2024
[21]

arXiv preprint arXiv:2512.09606 (2025)

Hao, Y., Liu, L., Wang, X., Han, Z., Wei, M., Zheng, Z., Tang, S.: A uni- fied framework for identifying influential nodes in hypergraphs. arXiv preprint arXiv:2512.09606 (2025)

2025
[22]

Nature communications17(1), 3253 (2026)

Oliveira, K.A., Traversa, P., Arruda, G., Moreno, Y.: Rumor propagation on hypergraphs. Nature communications17(1), 3253 (2026)

2026
[23]

Physical Review E 114(1), 014302 (2026)

Mancastroppa, M., Karsai, M., Barrat, A.: Higher-order adaptive behaviors out- perform pairwise strategies in mitigating contagion dynamics. Physical Review E 114(1), 014302 (2026)

2026
[24]

Physics Reports1018, 1–64 (2023)

Boccaletti, S., De Lellis, P., Genio, C.I., Alfaro-Bittner, K., Criado, R., Jalan, S., Romance, M.: The structure and dynamics of networks with higher order interactions. Physics Reports1018, 1–64 (2023)

2023
[25]

Physical Review E110(3), 034312 (2024)

Pister, A., Barthelemy, M.: Stochastic block hypergraph model. Physical Review E110(3), 034312 (2024)

2024
[26]

Physical Review Letters132(3), 037401 (2024)

Di Gaetano, L., Battiston, F., Starnini, M.: Percolation and topological proper- ties of temporal higher-order networks. Physical Review Letters132(3), 037401 (2024)

2024
[27]

arXiv preprint arXiv:2606.23289 (2026)

Hao, Y., Liu, L., Wang, X., Han, Z., Zheng, H., Tang, S.: Identifying vulnerable nodes for hypergraph dismantling via higher-order competition dynamics. arXiv preprint arXiv:2606.23289 (2026)

2026 arXiv
[28]

Nature communications16(1), 555 (2025)

Malizia, F., Lamata-Ot ´ ın, S., Frasca, M., Latora, V., G´ omez-Garde˜ nes, J.: Hyper- edge overlap drives explosive transitions in systems with higher-order interactions. Nature communications16(1), 555 (2025)

2025
[29]

Nature Reviews Physics8(3), 146–159 (2026)

Battiston, F., Bick, C., Lucas, M., Mill´ an, A.P., Skardal, P.S., Zhang, Y.: Collec- tive dynamics on higher-order networks. Nature Reviews Physics8(3), 146–159 (2026)

2026
[30]

15 Physical review letters127(15), 158301 (2021)

St-Onge, G., Sun, H., Allard, A., H´ ebert-Dufresne, L., Bianconi, G.: Universal nonlinear infection kernel from heterogeneous exposure on higher-order networks. 15 Physical review letters127(15), 158301 (2021)

2021
[31]

Chaos: An Interdisciplinary Journal of Nonlinear Science 34(2), 023137 (2024)

Han, Z., Liu, L., Wang, X., Hao, Y., Zheng, H., Tang, S., Zheng, Z.: Probabilis- tic activity driven model of temporal simplicial networks and its application on higher-order dynamics. Chaos: An Interdisciplinary Journal of Nonlinear Science 34(2), 023137 (2024)

2024
[32]

Physical Review Letters132(7), 077401 (2024)

Burgio, G., G´ omez, S., Arenas, A.: Triadic approximation reveals the role of interaction overlap on the spread of complex contagions on higher-order networks. Physical Review Letters132(7), 077401 (2024)

2024
[33]

Physical Review Letters135(20), 207401 (2025)

Malizia, F., Guzm´ an, A., Iacopini, I., Kiss, I.Z.: Disentangling the role of hetero- geneity and hyperedge overlap in explosive contagion on higher-order networks. Physical Review Letters135(20), 207401 (2025)

2025
[34]

Physical Review Research3(3), 033282 (2021)

Jhun, B.: Effective epidemic containment strategy in hypergraphs. Physical Review Research3(3), 033282 (2021)

2021
[35]

PNAS nexus2(3), 035 (2023)

Horta Ribeiro, M., Hosseinmardi, H., West, R., Watts, D.J., Contractor, N.,et al.: Deplatforming did not decrease parler users’ activity on fringe social media. PNAS nexus2(3), 035 (2023)

2023
[36]

PNAS nexus2(10), (2023)

Monti, C., Cinelli, M., Valensise, C., Quattrociocchi, W., Stamini, M.: Online conspiracy communities are more resilient to deplatforming. PNAS nexus2(10), (2023)

2023
[37]

Nature Communications16(1), 4589 (2025)

Burgio, G., St-Onge, G., H´ ebert-Dufresne, L.: Characteristic scales and adapta- tion in higher-order contagions. Nature Communications16(1), 4589 (2025)

2025
[38]

Physical Review Research8(3), 033068 (2026)

Liu, L., Zheng, H., Han, Z., Wang, X., Tang, S.: Inhibition of explosive transitions in spreading dynamics by nonlinear higher-order adaptation. Physical Review Research8(3), 033068 (2026)

2026
[39]

The journal of physical chemistry81(25), 2340–2361 (1977)

Gillespie, D.T.: Exact stochastic simulation of coupled chemical reactions. The journal of physical chemistry81(25), 2340–2361 (1977)

1977
[40]

Physics Reports1056, 1–70 (2024)

Wang, W., Nie, Y., Li, W., Lin, T., Shang, M.-S., Su, S., Tang, Y., Zhang, Y.-C., Sun, G.-Q.: Epidemic spreading on higher-order networks. Physics Reports1056, 1–70 (2024)

2024
[41]

PloS one10(9), 0136497 (2015)

Mastrandrea, R., Fournet, J., Barrat, A.: Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys. PloS one10(9), 0136497 (2015)

2015
[42]

G´ enois, M., Barrat, A.: Can co-location be used as a proxy for face-to-face contacts? EPJ Data Science7(1), 1–18 (2018) 16

2018
[43]

Physical Review E96(3), 032312 (2017)

Young, J.-G., Petri, G., Vaccarino, F., Patania, A.: Construction of and effi- cient sampling from the simplicial configuration model. Physical Review E96(3), 032312 (2017)

2017
[44]

arXiv preprint arXiv:2606.12013 (2026)

Liu, L., Zheng, H., Hao, Y., Wang, Q., Wang, X., Tang, S.: Emergent dilemma and periodic oscillation in the nonlinear interplay between epidemic and behavior. arXiv preprint arXiv:2606.12013 (2026)

2026 arXiv
[45]

Nature Human Behaviour5(5), 586–595 (2021)

Alvarez-Rodriguez, U., Battiston, F., Arruda, G.F., Moreno, Y., Perc, M., Latora, V.: Evolutionary dynamics of higher-order interactions in social networks. Nature Human Behaviour5(5), 586–595 (2021)

2021
[46]

Applied Mathematical Modelling, 116857 (2026)

Jiang, Y., Wang, X., Zhu, W., Wei, M., Liu, L., Tang, S., Zheng, H.: Nonlinear public goods game in dynamical environments. Applied Mathematical Modelling, 116857 (2026)

2026
[47]

Physical Review E111(3), 034302 (2025)

Lamata-Ot ´ ın, S., Malizia, F., Latora, V., Frasca, M., G´ omez-Garde˜ nes, J.: Hyper- edge overlap drives synchronizability of systems with higher-order interactions. Physical Review E111(3), 034302 (2025)

2025
[48]

chang et al

Chang, X., Dai, W., Zhang, J.-Q., Cai, C.-R., Tang, M.: Oscillatory dynam- ics driven induced by higher-order interactions and self-awareness in multiplex information–epidemic systems: X. chang et al. Nonlinear Dynamics114(9), 623 (2026)

2026
[49]

Physical Review Research6(3), 033003 (2024) 17

Pal, P.K., Anwar, M.S., Perc, M., Ghosh, D.: Global synchronization in gener- alized multilayer higher-order networks. Physical Review Research6(3), 033003 (2024) 17

2024

Pith tools

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

[1] [1]

Science359(6380), 1094–1096 (2018)

Lazer, D.M., Baum, M.A., Benkler, Y., Berinsky, A.J., Greenhill, K.M., Menczer, F., Metzger, M.J., Nyhan, B., Pennycook, G., Rothschild, D.,et al.: The science of fake news. Science359(6380), 1094–1096 (2018)

work page 2018

[2] [2]

Nature human behaviour5(3), 337–348 (2021)

Loomba, S., De Figueiredo, A., Piatek, S.J., De Graaf, K., Larson, H.J.: Measuring the impact of covid-19 vaccine misinformation on vaccination intent in the uk and usa. Nature human behaviour5(3), 337–348 (2021)

work page 2021

[3] [3]

Journal of the European Economic Association19(4), 2131–2167 (2021)

M¨ uller, K., Schwarz, C.: Fanning the flames of hate: Social media and hate crime. Journal of the European Economic Association19(4), 2131–2167 (2021)

work page 2021

[4] [4]

Science352(6292), 1459–1463 (2016) 13

Johnson, N.F., Zheng, M., Vorobyeva, Y., Gabriel, A., Qi, H., Vel´ asquez, N., Manrique, P., Johnson, D., Restrepo, E., Song, C.,et al.: New online ecology of adversarial aggregates: Isis and beyond. Science352(6292), 1459–1463 (2016) 13

work page 2016

[5] [5]

Proceedings of the national academy of Sciences113(3), 554–559 (2016)

Del Vicario, M., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., Stanley, H.E., Quattrociocchi, W.: The spreading of misinformation online. Proceedings of the national academy of Sciences113(3), 554–559 (2016)

work page 2016

[6] [6]

science 359(6380), 1146–1151 (2018)

Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. science 359(6380), 1146–1151 (2018)

2018

[7] [7]

Proceedings of the national academy of sciences118(9), 2023301118 (2021)

Cinelli, M., De Francisci Morales, G., Galeazzi, A., Quattrociocchi, W., Starnini, M.: The echo chamber effect on social media. Proceedings of the national academy of sciences118(9), 2023301118 (2021)

work page 2021

[8] [8]

Physical Review X10(4), 041042 (2020)

Wang, X., Sirianni, A.D., Tang, S., Zheng, Z., Fu, F.: Public discourse and social network echo chambers driven by socio-cognitive biases. Physical Review X10(4), 041042 (2020)

work page 2020