Membership of nodes in a diffusion

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(.data)

Arguments

.data

An object of a manynet-consistent class:

• matrix (adjacency or incidence) from {base} R

• edgelist, a data frame from {base} R or tibble from {tibble}

• igraph, from the {igraph} package

• network, from the {network} package

• tbl_graph, from the {tidygraph} package

References

On adopter classes

Valente, Tom W. 1995. Network models of the diffusion of innovations (2nd ed.). Cresskill N.J.: Hampton Press.

See also

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

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))
#> 4 groups
#>   V1     V2    V3    V4    V5    V6    V7    V8    V9    V10   V11   V12   V13  
#> 1 Early… Earl… Earl… Earl… Earl… Late… Late… Earl… 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, 2, 3, 14
#> Class Early Majority:  4, 5, 8, 12, 13, 15
#> Class Laggard:  9
#> Class Late Majority:  6, 7, 10, 11