| member_diffusion {manynet} | R Documentation |
Membership of nodes in a diffusion
Description
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
Usage
node_in_adopter(diff_model)
Arguments
diff_model |
A valid network diffusion model,
as created by |
References
Valente, Tom W. 1995. Network models of the diffusion of innovations (2nd ed.). Cresskill N.J.: Hampton Press.
See Also
Other measures:
between_centrality,
close_centrality,
degree_centrality,
eigenv_centrality,
measure_attributes,
measure_closure,
measure_cohesion,
measure_features,
measure_heterogeneity,
measure_hierarchy,
measure_holes,
measure_infection,
measure_net_diffusion,
measure_node_diffusion,
measure_periods,
measure_properties
Other diffusion:
measure_infection,
measure_net_diffusion,
measure_node_diffusion,
play
Examples
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))
summary(adopts)