Abstract

PurposeThe paper aims to propose the use of spline functions for the description and visualization of discrete informetric data.Design/methodology/approachInterpolating cubic splines: are interpolating functions (they pass through the given data points); are cubic, i.e. are polynomials of third degree; have first and second derivatives in the data points, implying that they connect data points in a smooth way; satisfy a best‐approximation property which tends to reduce curvature. These properties are illustrated in the paper using real citation data.FindingsThe paper reveals that calculating splines yields a differentiable function that still captures small but real changes. It offers a middle way between connecting discrete data by line segments and providing an overall best‐fitting curve.Research limitations/implicationsThe major disadvantage of the use of splines is that accurate data are essential.Practical implicationsSpline functions can be used for illustrative as well as modelling purposes.Originality/valueSplines have hardly ever been used or studied in the information sciences.

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