Graph traversal algorithms, like BFS and DFS, are used to explore nodes and edges in graphs. They're essential in problems involving paths, connectivity, and cycles.

Graph traversal algorithms are fundamental in competitive programming for solving problems that involve exploring graphs, which are made up of nodes (vertices) and edges (connections between nodes). Two of the most commonly used traversal algorithms are Breadth-First Search (BFS) and Depth-First Search (DFS). BFS explores all nodes at the present depth level before moving on to nodes at the next level, making it ideal for finding the shortest path in unweighted graphs. It's widely used in problems like finding the shortest route in a maze or a grid. DFS, on the other hand, explores as far as possible along a branch before backtracking, which makes it useful for checking connectivity, detecting cycles, and solving maze problems. DFS can also be implemented using recursion, which makes it simpler to code but potentially more memory-intensive. In addition to these, there are specialized algorithms like Dijkstra’s and A* for weighted graphs, where the edge weights represent distances or costs. Graph traversal algorithms are critical for solving problems involving road networks, social networks, web crawling, and more, so mastering them is a must for competitive programmers.