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Breadth-first search (BFS) and depth-first search (DFS) are both algorithms used for traversing or searching tree or graph data structures. However, they differ in their exploration strategies:
Breadth-First Search (BFS):
BFS explores all the neighbor nodes at the present depth before moving on to the nodes at the next depth level.
It uses a FIFO (First-In-First-Out) queue data structure to keep track of the nodes to be explored.
BFS is often used to find the shortest path in an unweighted graph because it explores all nodes at a given depth level before moving to the next depth level.
BFS guarantees finding the shortest path if the graph is unweighted.
Depth-First Search (DFS):
DFS explores as far as possible along each branch before backtracking.
It uses a stack data structure or recursive function calls to keep track of the nodes to be explored.
DFS is often used for topological sorting, cycle detection, and finding strongly connected components in a graph.
Unlike BFS, DFS does not guarantee finding the shortest path, and it may get trapped in infinite loops in the presence of cycles.
In summary, BFS is suitable for finding the shortest path in unweighted graphs and systematically exploring all nodes at each level, while DFS is suitable for exploring deeply into a graph and solving problems like topological sorting and cycle detection.