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Table 7 Boundary point detection algorithm for general functions

From: Feature analysis of multidisciplinary scientific collaboration patterns based on PNAS

Input: Data vector \(h_{0}(s)\) (s = 1,…,K), rescaling function g(), and fitting model h().
For k from 1 to K do:
 Fit h() to \(h_{0}(s)\), s = 1,…,k by regression;
 Do KS test for two data vectors g(h(s)) and \(g( h_{0}(s))\), s = 1,…,k with the null hypothesis they coming from the same distribution;
 Break if the test rejects the null hypothesis at significance level 5%.
Output: The current k as the boundary point.