Dijkstra’s Algorithm with Bellman-Ford for Shortest Path Discovery in Big Data

Many applications, from logistics to social networks, rely on quickly navigating the shortest pathways inside large-scale graphs. For shortest route finding in huge data settings, this study aims to compare Dijkstra’s method with the Bellman-Ford algorithm. Applying these methods to large datasets will allow us to compare their speed, accuracy, and scalability. Graph density and negative weight cycles are two of the intrinsic properties of big data settings that will be used to determine which method is most suited to each. Optimal algorithm selection for given data situations is guided by insights into each algorithm’s operational strengths and shortcomings, which are revealed via rigorous testing and performance metrics analysis. This comparison helps real-world applications of each method and provides valuable information for future advancements in large data processing shortest route calculation algorithms. In a study comparing Dijkstra’s Algorithm and Bellman-Ford using the RandomGraph1 dataset, the results showed that for nodes $100-500$, the former’s ms value ranged from 5.2 to 52.3 and the latter’s ms value ranged from 6.8 to 68.9. Similarly, for nodes 100-500, the MB value range for Dijkstra’s Algorithm was 10.2-46.7 and for Bellman-Ford, it was 14.8-71.9. Data from a different SparseGraph1 dataset reveals that, for values between 100 and 500, Dijkstra’s Algorithm has a percentage range of 98.5 to 99.8, while for Bellman-Ford, it varies from 97.2 to 99.3.