Network-Based and Binless Frequency Analyses

Abstract

We introduce and develop a new network-based and binless methodology to perform frequency analyses and produce histograms. In contrast with traditional frequency analysis techniques that use fixed intervals to bin values, we place a range ± ζ around each individual value in a data set and count the number of values within that range, which allows us to compare every single value of a data set with one another. In essence, the methodology is identical to the construction of a network, where two values are connected if they lie within a given a range (± ζ). The value with the highest degree (i.e., most connections) is therefore assimilated to the mode of the distribution. To select an optimal range, we look at the stability of the proportion of nodes in the largest cluster. The methodology is validated by sampling 12 typical distributions, and it is applied to a number of real-world data sets with both spatial and temporal components. The methodology can be applied to any data set and provides a robust means to uncover meaningful patterns and trends. A free python script and a tutorial are also made available to facilitate the application of the method.