The PageRank algorithm is named after Larry Page, one of the co-founders of Google. The algorithm was developed back in 1996 in order to rank the results of a search engine. In this section, we will understand the formula by building it step by step. We will then run the algorithm on a single graph to see how it converges. We will also implement a version of the algorithm using Python. Finally, we will learn how to use GDS to get this information from a graph stored in Neo4j.
Building the formula
Let's consider PageRank in the context of the internet. The PageRank algorithm relies on the idea that not all incoming links have the same weight. As an example, consider a backlink from a New York Times article to an article in your blog. It is more important than a link from a website that gets 10 visits a month since it will redirect more users to your blog. So, we would like the New York Times to have more weight than the low-traffic website. The...
