What is the difference between deep search and shallow search?

Deep search and shallow search refer to different strategies for exploring data structures like trees or graphs. The key difference lies in how they prioritize exploring nodes: deep search (depth-first search) explores as far as possible along a branch before backtracking, while shallow search (breadth-first search) explores all nodes at the current level before moving deeper. For example, in a tree, depth-first might go to the leftmost leaf first, while breadth-first would visit all nodes level by level, starting from the root.

Shallow search is typically implemented using a queue to track nodes, ensuring that earlier-discovered nodes are explored first. This makes it well-suited for scenarios where the shortest path or immediate neighbors matter, like finding the closest match in a network. For instance, finding the shortest path in an unweighted graph benefits from BFS because it guarantees the first time a node is visited is via the shortest route. In contrast, deep search uses a stack (or recursion) to prioritize the most recently discovered nodes, making it useful for exhaustive exploration, such as checking all possible configurations in a puzzle or traversing a file system hierarchy.

The choice between the two depends on the problem’s requirements. Shallow search is efficient for localized queries but can consume more memory when handling wide structures (e.g., a social network with many connections). Deep search uses less memory in narrow, deep structures but risks infinite loops in cyclic graphs without proper cycle detection. For example, a directory traversal tool might use DFS to list nested folders, while a recommendation system might use BFS to find nearby users. Developers should weigh memory constraints, completeness needs, and the nature of the data structure when selecting an approach.