Liu et al.'s finite-time dynamics-sensitive (DS) centrality is \(S(T)=\sum_{r=0}^{T-1}\beta A[\beta A+(1-\mu)I]^r\mathbf{1}\), where beta is the spreading rate and mu the recovery rate (equation 5 in the preprint). This is the full recovery-parameter family. For mu=1, it reduces to \(\sum_{t=1}^{T}(\beta A)^t\mathbf{1}\) (equation 7), also the form listed in the Centrality Zoo. For mu=0 it gives the paper's susceptible-infected case.
centrality_dynamics_sensitive(x, ds_beta = 0.1, ds_mu = 1, ds_steps = 5, ...)Named numeric vector in input node order.
Network input accepted by centrality.
Finite spreading rate between 0 and 1, default 0.1.
Finite recovery rate between 0 and 1, default 1.
Nonnegative integer horizon, default 5. Must not exceed
.Machine$integer.max.
Additional arguments to centrality. With
normalized = TRUE, positive scores are divided by their maximum.
Uses the simple undirected unweighted skeleton, as in the source: either
direction creates an edge, parallel edges count once and loops are removed.
The projection of other inputs is an explicit cograph convention.
mode, edge weights and shortest-path weight inversion do not affect
this measure. Isolates score zero. T=0 or beta=0 returns zero; T=1 gives
beta times degree. The initial seed itself is not added to the score.
This linearized cumulative spreading score allows repeated walks and can exceed the number of nodes. It is not a bounded infection probability or an exact simulation of the nonlinear SIR/SI process. Defaults beta=0.1, mu=1 and T=5 select a parameter setting studied in the paper; they are not fitted to the input network. Any finite horizon is supported without a spectral convergence condition, subject to numerical precision. Overflow raises an error, even if normalization is requested.
Liu, J. G., Lin, J. H., Guo, Q., & Zhou, T. (2016). Locating influential nodes via dynamics-sensitive centrality. Scientific Reports, 6, 21380. tools:::Rd_expr_doi("10.1038/srep21380").
centrality_diffusion_centrality.
g <- igraph::make_ring(5)
centrality_dynamics_sensitive(g, ds_beta = 0.1, ds_mu = 1, ds_steps = 5)
centrality_dynamics_sensitive(g, ds_mu = 0)
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