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Fabrizio Musacchio
Fabrizio Musacchio
@FabMusacchio@mastodon.social  ·  activity timestamp last week

🧠 New preprint on adaptive contagion dynamics on #hypergraphs by Mancastroppa, Karsai, & Barrat.

The key result: adaptive, locally informed behavior can neutralize explosive phase transitions in higher-order contagion. Group-level awareness suppresses nonlinear reinforcement, shrinks bistability, and can turn a discontinuous transition into a continuous one. A nice mechanistic link between adaptivity, higher-order interactions, and phase transitions.

#ComplexSystems #NonlinearDynamics

FIG. 1. Schematic representation of contagion and adaptation mechanisms. a: example of a hypergraph H and the corresponding weighted projected graph G. b: the SIS contagion mechanisms for the node i, with pairwise (bottom) and higher-order (top) processes. c: adaptive strategies for node i, with group-based awareness (top, ng with θ = 0.3:
f ng i (t) = 3/⟨D⟩) or pairwise-based awareness (bottom, nn: fnn i (t) = 4/⟨k⟩).
FIG. 1. Schematic representation of contagion and adaptation mechanisms. a: example of a hypergraph H and the corresponding weighted projected graph G. b: the SIS contagion mechanisms for the node i, with pairwise (bottom) and higher-order (top) processes. c: adaptive strategies for node i, with group-based awareness (top, ng with θ = 0.3: f ng i (t) = 3/⟨D⟩) or pairwise-based awareness (bottom, nn: fnn i (t) = 4/⟨k⟩).
FIG. 1. Schematic representation of contagion and adaptation mechanisms. a: example of a hypergraph H and the corresponding weighted projected graph G. b: the SIS contagion mechanisms for the node i, with pairwise (bottom) and higher-order (top) processes. c: adaptive strategies for node i, with group-based awareness (top, ng with θ = 0.3: f ng i (t) = 3/⟨D⟩) or pairwise-based awareness (bottom, nn: fnn i (t) = 4/⟨k⟩).
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