How does Depth-First Search (DFS) differ from Breadth-First Search (BFS)?

Depth-First Search (DFS) is characterized by its approach to exploring a graph or tree. DFS operates by choosing a starting node and then exploring as far down a branch as possible before backtracking. This means it traverses nodes and explores each path to its deepest point before moving to the next available path.

This method results in a stack-like structure, often implemented using recursion or an explicit stack. Therefore, it's particularly effective when seeking solutions that require going deep into one branch of a structure without being constrained by nearby nodes.

In contrast, the other descriptions do not accurately depict the unique characteristics of DFS. For instance, DFS does not explore all neighbors first; that would be a characteristic of Breadth-First Search (BFS), which examines all neighbors at the present depth before moving on to nodes at the next level. Moreover, while memory usage can vary depending on the graph structure, BFS typically uses more memory due to its need to hold all sibling nodes at the current level in a queue. Lastly, speed is not solely determined by the algorithm type; BFS is not inherently faster than DFS, as performance can depend on factors like the graph's structure and specific goals in searching.