PageRank, a pivotal algorithm initially developed by Google, is instrumental in measuring influence within interconnected data networks, such as social media interactions or financial transactions, and is adaptable for identifying influential entities or potential bottlenecks. Traditional applications of PageRank on static datasets are being challenged by the increasing volume and dynamism of modern data. To address this, incremental updates to PageRank allow real-time data processing by recalculating influence measurements only on affected data points, thus enhancing efficiency and reducing computational demands. This approach is based on techniques such as approximative PageRank, which rely on sampling random walks to estimate influence. The method, championed by research from Twitter employees, is crucial for companies dealing with streaming data, enabling them to derive insights more swiftly and effectively. The exploration of graph analytics algorithms for real-time data is ongoing, with opportunities for further discussion and collaboration in the domain.