PageRank is an algorithm that was originally developed by the founders of Google as a way of ranking web pages in terms of importance and influence across the internet. The output of the PageRank algorithm is a numeric value between 0 and 1 for each node. The algorithm will add a column to the existing dataset and that column can be mapped to any dimension you desire.
The algorithm works iteratively. Initially, all nodes in the network are assigned an equal amount of PageRank. In each iteration, each node will equally distribute its current PageRank with its neighbors (if edges are weighted, more PageRank will be shared with neighbors that share a larger edge weight). After several iterations, this provides a metric where nodes with high PageRank are generally connected to other nodes with high PageRank.
Network analytics metrics tend to follow a power distribution, which is why we show color with a gradient and automatically normalize the color dimension to get a nice spread of colors.