Exploring Cooperative Game Mechanisms of Scientific Coauthorship Networks

Algorithm 1

A boundary detection algorithm for probability density functions.

Input: Observations D_s, s = 1, … ,n, rescaling function g(·), and fitting model h(·).

For k from 1 to max (D₁, … ,D_n) do:

 Fit h(·) to the PDF h₀(·) of {D_s, s = 1, … ,n|D_s ≤ k} by maximum-likelihood estimation;

 Do KS test for two data g (h (t)) and g (h₀(t)), t = 1, … ,k with the null hypothesis they coming from the same continuous distribution;

 Break if the test rejects the null hypothesis at significance level 5%. Output: The current k as the boundary point.