DFS vs. BFS: What Is the Difference?

As a computer science student, I know how important it is to understand tree traversal algorithms. I suggest every beginner should clarify the basic differences between these two algorithms.

Breadth-first search (BFS) and depth-first search (DFS) are the most popular tree traversal algorithms. Both techniques include visiting all the edges and vertices of a graph but the most important difference between them is that they perform different data structures. BFS applies queue data structure and DFS applies the stack data structure. Depending on this difference, we can determine between the two methods which one is appropriate for a particular purpose.

In this article, I will discuss the key differences between DFS and BFS.

What Is Depth-First Search?

Depth-first search is a recursive algorithm that is used to find trees or graph a data structure. This algorithm uses the concept of backtracking and searches in-depth ward direction. The implementation of this algorithm starts at the root node and searches as far as possible before the backtracking process. DFS uses the stack data structure to recall when a dead-end comes to pick the further nodes. Learn more about the depth-first search algorithm here.

What Is Breadth-First Search?

The breadth-first search algorithm is a non-recursive algorithm used to search or traverse trees or graph a data structure. The implementations of this algorithm begin by selecting a single node or tree root as the initial point and exploring all the neighbor nodes. After the visit and marking, the starting node the algorithm progresses toward the unvisited nearest nodes and analyzes them. BFS algorithm uses the queue data structure to check whether a node has been found out before enqueueing the node. This algorithm explores the nearest node branch as far as possible before the backtracking process; it is exactly the opposite strategy of depth-first search. Learn more about the breadth-first search algorithm here.

DFS vs. BFS: Key Differences