get.infectiousness: Estimate the infectiousness of an information cascade

In seismic: Predict Information Cascade by Self-Exciting Point Process

Estimate the infectiousness of an information cascade

Description

Estimate the infectiousness of an information cascade

Usage

get.infectiousness(
  share.time,
  degree,
  p.time,
  max.window = 2 * 60 * 60,
  min.window = 300,
  min.count = 5
)

Arguments

share.time	observed resharing times, sorted, share.time[1] =0
degree	observed node degrees
p.time	equally spaced vector of time to estimate the infectiousness, p.time[1]=0
max.window	maximum span of the locally weight kernel
min.window	minimum span of the locally weight kernel
min.count	the minimum number of resharings included in the window

Details

Use a triangular kernel with shape changing over time. At time p.time, use a triangluer kernel with slope = min(max(1/(p.time/2), 1/min.window), max.window).

Value

a list of three vectors:

• infectiousness. the estimated infectiousness

• p.up. the upper 95 percent approximate confidence interval

• p.low. the lower 95 percent approximate confidence interval

Examples

data(tweet)
pred.time <- seq(0, 6 * 60 * 60, by = 60)
infectiousness <- get.infectiousness(tweet[, 1], tweet[, 2], pred.time)
plot(pred.time, infectiousness$infectiousness)

seismic documentation built on May 21, 2022, 1:05 a.m.