The most interesting and famous application of PageRank is certainly the one that actually sparked its creation. Google founders Larry Page and Sergey Brin needed an algorithm to rank pages and provide users with the best possible search results.

Using the PageRank algorithm, each page receives a ranking based on the number and importance of other pages that are linking to it. The pages with a higher page rank, increase the ranking of the page they link to more than the pages with a lower rank.

In graph database terminology, the PageRank algorithm is used to measure the importance of each node based on the number of incoming relationships and the rank of the related source nodes. What the PageRank algorithm actually outputs is a probability distribution that represents the likelihood of visiting any particular node by randomly traversing the graph.

So, it’s basically a node popularity contest.

A widely used type of PageRank is Personalized PageRank, which is extremely useful in recommendation systems. With Personalized PageRank, you can restrain the random walk by allowing it to start only from one of the nodes in a given set, and jump only to one of the nodes in a given set. This type of PageRank brings out central nodes from the perspective of that set of specific nodes. For example, Twitter uses Personalized PageRank to recommend who to follow online.

The animation below shows the results of PageRank on a simple network. A sequel of a well-liked movie will automatically be more popular than just a random new title because it already has an established fan base. In graph terms, the biggest node pointing to an adjacent node makes it more important.

PageRank can be used as a measure of influence that can be used on a variety of applications, not just on website and movie rankings.

PageRank use cases

If a social network or a search engine are not the products you are developing, check out how you can utilize PageRank in various other use cases or knowledge graphs built to infer knowledge in these niches.

Recommendation Engines

In Recommendation Engines, PageRank algorithm can be utilized to recommend products that match the target user’s preferences or are currently trending among all the other users. The algorithm considers the number of purchases and the reliability of the users who bought or reviewed the product.

A reliable user has a valid usage history and reviews, while unreliable users are fake customers whose purpose is to artificially inflate the metrics of certain products to make them appea