hyper-VDrank ranks nodes via higher-order competition dynamics and hyperedge weights to dismantle hypergraphs faster under strong deletion, improving efficiency 23.65% and lowering collapse threshold 27.63% on 14 real hypergraphs versus baselines.
Higher-order network adaptivity: co-evolution of higher-order structure and spreading dynamics.arXiv preprint arXiv:2508.15445, 2025
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A co-evolutionary model of epidemics and nonlinear behavioral responses reveals an NPI-abandonment social dilemma and periodic oscillations induced by social influence.
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Identifying vulnerable nodes for hypergraph dismantling via higher-order competition dynamics
hyper-VDrank ranks nodes via higher-order competition dynamics and hyperedge weights to dismantle hypergraphs faster under strong deletion, improving efficiency 23.65% and lowering collapse threshold 27.63% on 14 real hypergraphs versus baselines.
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Emergent dilemma and periodic oscillation in the nonlinear interplay between epidemic and behavior
A co-evolutionary model of epidemics and nonlinear behavioral responses reveals an NPI-abandonment social dilemma and periodic oscillations induced by social influence.