Fig. 8
From: Fractal dimension analogous scale-invariant derivative of Hirsch’s index
An intuitive outline of the algorithm. The vertical axis is for the rank, up for larger, or lower rank. The horizontal axis shows the value of the data. Purple curve shows the empirical distribution function of the data. For the aid of comparison between the value and its rank, green diagonal straight line is added. To calculate the h-index value is to find out the point where the rank and value is equal (or nearest), which is the crossing point H. In the beginning, \(P_0\) is randomly selected from the whole data. Hence \(P_0\) has larger rank than the value itself, H must be in the segment \(I_0, (\ge P_0)\). Next value \(P_1\) is randomly selected from \(I_0\). \(P_1\) has a smaller rank than its value, therefore H should be in the segment \(I_1, (\le P_1)\). Then, \(P_2\), which has larger rank than its value is randomly selected from \(I_1\). \(P_2\) sets the next segment \(I_2\). Repeat the process two more times to finally reach H. Black slithering curve at the bottom of the figure depicts the search path