Big Data Graph Node Importance Using Page Rank

Improving the efficiency and accuracy of finding important nodes in large networks is the goal of enhancing Big Data graph node significance analysis using PageRank. To deal with the massive and complicated data sets seen in big data settings, it is necessary to modify and enhance the PageRank algorithm. The objective is to provide a more efficient framework that can speed up computation and improve the accuracy of significance ranking in large-scale networks. To control the algorithm's scalability, it is necessary to use state-of-the-art computational methods, such as parallel processing and effective data structures. The expected result is a scalable and reliable system that can quickly and correctly analyze large networks, yielding useful insights for applications like recommendation systems, online search, and social network analysis. The focus is on making PageRank more feasible for use in large data settings while also increasing its computing efficiency and scalability. The Adjacency Matrix of the Graph in a sample of 5 nodes has a value ranging from 15 to 55 in numbers, according to results from synthetic graph data. In another instance of the same dataset, the Initial PageRank Scores range from 0.2-0.15 in numbers, and in yet another instance, the final PageRank Scores for nodes range from 0.177 to 0.242 in numbers.