Comparing Traversal Strategies: Depth-first Search vs. Breadth-first Search in Complex Networks

This article compares and contrasts two basic graph traversal algorithms that are commonly employed in computational problem-solving and network research. Common applications of these algorithms include pathfinding, optimisation of network flows, collaborative exploration, and classification tasks. To find out how well they function with different types of datasets, network topologies, and issue domains, researchers have systematically reviewed previous works. We measured the efficiency of each solution using performance indicators like execution time, memory utilisation, and path length. According to the results, one approach is more effective in memory-constrained settings and deep searches, while the other is better at discovering the shortest paths and providing comprehensive coverage. Furthermore, the paper emphasises the advantages of hybrid techniques, which merge the best features of both algorithms to provide better results in specific cases. This comparison helps fill gaps in our knowledge of graph-based problem-solving methods and sheds light on how to choose the best traversal algorithms for different types of applications.