node_diffusion  R Documentation 
These functions allow measurement of various features of a diffusion process:
node_adoption_time()
: Measures the number of time steps until
nodes adopt/become infected
node_adopter()
: Classifies membership of nodes into diffusion categories
node_thresholds()
: Measures nodes' thresholds from the amount
of exposure they had when they became infected
node_infection_length()
: Measures the average length nodes that become
infected remain infected in a compartmental model with recovery
node_exposure()
: Measures how many exposures nodes have to
a given mark
node_is_exposed()
: Marks the nodes that are susceptible,
i.e. are in the immediate neighbourhood of given mark vector
node_adoption_time(diff_model)
node_adopter(diff_model)
node_thresholds(diff_model)
node_infection_length(diff_model)
node_exposure(.data, mark, time = 0)
diff_model 
A valid network diffusion model,
as created by 
.data 
An object of a

mark 
A valid 'node_mark' object or logical vector (TRUE/FALSE) of length equal to the number of nodes in the network. 
time 
A time point until which infections/adoptions should be
identified. By default 
node_adoption_time()
measures the time units it took
until each node became infected.
Note that an adoption time of 0 indicates that this was a seed node.
node_adopter()
classifies the nodes involved in a diffusion
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
Nonadopter: those without an adoption time, i.e. never adopted
node_thresholds()
infers nodes' thresholds based on how much
exposure they had when they were infected.
This inference is of course imperfect,
especially where there is a sudden increase in exposure,
but it can be used heuristically.
node_infection_length()
measures the average length of time that nodes
that become infected remain infected in a compartmental model with recovery.
Infections that are not concluded by the end of the study period are
calculated as infinite.
node_exposure()
calculates the number of infected/adopting nodes
to which each susceptible node is exposed.
It usually expects network data and
an index or mark (TRUE/FALSE) vector of those nodes which are currently infected,
but if a diff_model is supplied instead it will return
nodes exposure at t = 0
.
Valente, Tom W. 1995. Network models of the diffusion of innovations (2nd ed.). Cresskill N.J.: Hampton Press.
Other measures:
between_centrality
,
close_centrality
,
closure
,
cohesion()
,
degree_centrality
,
eigenv_centrality
,
features
,
heterogeneity
,
hierarchy
,
holes
,
net_diffusion
,
periods
Other diffusion:
net_diffusion
smeg < manynet::generate_smallworld(15, 0.025)
smeg_diff < play_diffusion(smeg, recovery = 0.2)
plot(smeg_diff)
# To measure when nodes adopted a diffusion/were infected
(times < node_adoption_time(smeg_diff))
# To classify nodes by their position in the adoption curve
(adopts < node_adopter(smeg_diff))
summary(adopts)
summary(times, membership = adopts)
# To infer nodes' thresholds
node_thresholds(smeg_diff)
# To measure how long each node remains infected for
node_infection_length(smeg_diff)
# To measure how much exposure nodes have to a given mark
node_exposure(smeg, mark = c(1,3))
node_exposure(smeg_diff)
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