Erik Marsja - Python and R as tools of data analysis and building psychological experiments
Kernel Density Estimation-based Edge Bundling
Graph Bundling by Kernel Density Estimation
In statistics, kernel density estimation (KDE) is a non-parametric way to estimate the probability density function of a random variable. Kernel density estimation is a fundamental data smoothing problem where inferences about the population are made, based on a finite data sample.