Name and briefly describe two graph traversal algorithms and their typical use cases.

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Multiple Choice

Name and briefly describe two graph traversal algorithms and their typical use cases.

Depth-first search dives as deep as possible along a branch before backtracking, which makes it ideal for exploring an entire connected component and for tasks that require visiting nodes within a component thoroughly (like detecting cycles or performing a topological sort). Breadth-first search expands outward from the start node level by level, visiting all nodes at distance d before those at distance d+1, so in an unweighted graph it guarantees that the first time a node is reached you’ve found a shortest path from the source. So, use DFS to explore components and BFS to find shortest paths in unweighted graphs.

Depth-first search dives as deep as possible along a branch before backtracking, which makes it ideal for exploring an entire connected component and for tasks that require visiting nodes within a component thoroughly (like detecting cycles or performing a topological sort). Breadth-first search expands outward from the start node level by level, visiting all nodes at distance d before those at distance d+1, so in an unweighted graph it guarantees that the first time a node is reached you’ve found a shortest path from the source. So, use DFS to explore components and BFS to find shortest paths in unweighted graphs.

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