node_in_adopter() classifies membership of nodes into diffusion categories
by where on the distribution of adopters they fell.
Valente (1995) defines five memberships:
Early adopter: those with an adoption time less than the average adoption time minus one standard deviation of adoptions times
Early majority: those with an adoption time between the average adoption time and the average adoption time minus one standard deviation of adoptions times
Late majority: those with an adoption time between the average adoption time and the average adoption time plus one standard deviation of adoptions times
Laggard: those with an adoption time greater than the average adoption time plus one standard deviation of adoptions times
Non-adopter: those without an adoption time, i.e. never adopted
node_in_adopter(diff_model)A valid network diffusion model,
as created by as_diffusion() or play_diffusion().
Other measures:
measure_attributes,
measure_central_between,
measure_central_close,
measure_central_degree,
measure_central_eigen,
measure_closure,
measure_cohesion,
measure_diffusion_infection,
measure_diffusion_net,
measure_diffusion_node,
measure_features,
measure_heterogeneity,
measure_hierarchy,
measure_holes,
measure_periods,
measure_properties
Other diffusion:
make_play,
measure_diffusion_infection,
measure_diffusion_net,
measure_diffusion_node
smeg <- generate_smallworld(15, 0.025)
smeg_diff <- play_diffusion(smeg, recovery = 0.2)
# To classify nodes by their position in the adoption curve
(adopts <- node_in_adopter(smeg_diff))
#> 4 groups
#> V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13
#> 1 Early… Earl… Earl… Earl… Earl… Late… Late… Lagg… Lagg… Late… Late… Earl… Earl…
#> # ... with 2 more values from this nodeset unprinted. Use `print(..., n = Inf)` to print all values.
summary(adopts)
#> Class Early Adopter: 1
#> Class Early Majority: 2, 3, 4, 5, 12, 13, 14, 15
#> Class Laggard: 8, 9
#> Class Late Majority: 6, 7, 10, 11