Sequential cascades on random hypergraphs

Sequential decisions within a group can correlate individual adoption outcomes, but this microscopic dependence is usually replaced by an imposed group-level response. We study a one-shot cascade on sparse random hypergraphs in which the first target in a newly triggered hyperedge adopts with probability p, whereas each later target adopts with probability p + Δ after a success and p - δ after a failure. For this stated process, we derive the exact target marginal qm, the complete correlated group-offspring probability-generating function Fm(z), and the early branching factor B. The classical Galton-Watson criterion then places the infinitesimal-seed onset at B=1, while its extinction fixed point depends on the complete offspring law. The new ingredients are the exact transfer-matrix offspring law for outcome memory and a matched-independent hypergraph control with the same qm and onset. For the original-cardinality null, directed-incidence percolation gives Rnull = ρnullSnull; the forward-survival and backward-reachability maps coincide for pairwise groups but generally differ for larger groups. Monte Carlo tests across group-size and degree laws support the threshold and survival predictions. A collision-aware control, which rematches the null to the number of targets actually remaining at first processing, reduces but does not remove the observed final-size gap at the tested parameters. System-size and cutoff sweeps likewise show that the 5% finite-network indicator is a proxy rather than the branching-survival probability. All claims are conditional on one-shot triggering, a vanishing seed density, and locally tree-like topology.

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Publication Details

Journal
Chaos An Interdisciplinary Journal of Nonlinear Science
Published
2026-11-01
DOI
https://doi.org/10.1063/5.0355277
Primary Topic
Complex Network Analysis Techniques
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article
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article

Sequential cascades on random hypergraphs

Fanyuan Meng, Yiqi Zhang
Chaos An Interdisciplinary Journal of Nonlinear Science
Complex Network Analysis Techniques
article

Sequential cascades on random hypergraphs

Fanyuan Meng, Yiqi Zhang
article en

Abstract

Sequential decisions within a group can correlate individual adoption outcomes, but this microscopic dependence is usually replaced by an imposed group-level response. We study a one-shot cascade on sparse random hypergraphs in which the first target in a newly triggered hyperedge adopts with probability p, whereas each later target adopts with probability p + Δ after a success and p - δ after a failure. For this stated process, we derive the exact target marginal qm, the complete correlated group-offspring probability-generating function Fm(z), and the early branching factor B. The classical Galton-Watson criterion then places the infinitesimal-seed onset at B=1, while its extinction fixed point depends on the complete offspring law. The new ingredients are the exact transfer-matrix offspring law for outcome memory and a matched-independent hypergraph control with the same qm and onset. For the original-cardinality null, directed-incidence percolation gives Rnull = ρnullSnull; the forward-survival and backward-reachability maps coincide for pairwise groups but generally differ for larger groups. Monte Carlo tests across group-size and degree laws support the threshold and survival predictions. A collision-aware control, which rematches the null to the number of targets actually remaining at first processing, reduces but does not remove the observed final-size gap at the tested parameters. System-size and cutoff sweeps likewise show that the 5% finite-network indicator is a proxy rather than the branching-survival probability. All claims are conditional on one-shot triggering, a vanishing seed density, and locally tree-like topology.

Chaos An Interdisciplinary Journal of Nonlinear ScienceVol. 36(11)
Hangzhou Normal University (CN)
Openalex Percentile: Top 14%
Complex Network Analysis Techniques
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Sequential cascades on random hypergraphs — Fanyuan Meng, Yiqi Zhang · Chaos An Interdisciplinary Journal of Nonlinear Science (2026) | TGRS Research Map | TGRS