member_diffusion: Memberships in a diffusion process
In netrics: Many Ways to Measure and Classify Membership for Networks, Nodes, and Ties

member_diffusion

R Documentation

Memberships in a diffusion process

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(.data)

Arguments

.data

A network object of class mnet, igraph, tbl_graph, network, or similar. For more information on the standard coercion possible, see manynet::as_tidygraph().

Value

A node_member character vector the length of the nodes in the network, of group memberships "A", "B", etc for each node. If the network is labelled, then the assignments will be labelled with the nodes' names.

References

On adopter classes

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

Other nodal: mark_core, mark_degree, mark_diff, mark_nodes, mark_select_node, measure_assort_node, measure_broker_node, measure_brokerage, measure_central_between, measure_central_close, measure_central_degree, measure_central_eigen, measure_closure_node, measure_core, measure_diffusion_node, measure_diverse_node, member_brokerage, member_cliques, member_community, member_community_hier, member_community_non, member_components, member_core, member_equivalence, motif_brokerage_node, motif_exposure, motif_node, motif_path

Other diffusion: mark_diff, measure_diffusion_infection, measure_diffusion_net, measure_diffusion_node, motif_exposure, motif_hazard

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)

netrics documentation built on July 24, 2026, 5:07 p.m.