Estimate the infectiousness of an information cascade

Description

Estimate the infectiousness of an information cascade

Usage

1
2
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

1
2
3
4
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)