The PageRank algorithm, developed by Larry Page and Sergey Brinn in 1996, is a key concept in computer science that determines the popularity of web pages based on the number of links pointing to them. The algorithm has been improved upon by Kenny Bastani, who introduced the concept of Categorical PageRank, which breaks down PageRank into categories or partitions to analyze causality and understand contributing factors to changes in PageRank values over time. Bastani's design uses a property graph data model to partition nodes into pages and categories, allowing for scalable distribution of PageRank jobs using Apache Spark and Neo4j, enabling the analysis of subgraphs that describe each category. The proof of concept demonstrates the feasibility of this approach, and the resulting system can be used to analyze Wikipedia articles and other knowledge graphs.