Table 1.
Summary of the existing attachment kernel estimation methods.
Fig 1.
Estimation of the attachment kernel when the true model is Ak = 3(log(max(k, 1)))2 + 1.
A: Jeong’s method. B: Newman’s method. C: Corrected Newman’s method. D: PAFit. The solid line depicts the true model. The plots are on a log-log scale. The gray vertical lines are the estimated confidence intervals of the estimated values by PAFit. Confidence intervals are not available in the remaining methods.
Table 2.
Summary of true attachment kernels used in the Monte Carlo simulation.
Fig 2.
Comparison between five methods in average relative error.
A: B = 100. B: B = 20. See Table 2 for the details of the true attachment kernels Ak used here.
Table 3.
Summary statistics for the Flickr social network dataset.
Fig 3.
Estimation of the attachment kernel in the Flickr social network dataset.
A: Jeong’s method. B: Newman’s method. C: Corrected Newman’s method. D: PAFit. The plots are on a log-log scale. The solid line corresponding to Ak = k is plotted as a visual guide.