Graph Traversal Algorithms

There are two main types of graph traversal algorithms: Depth-First Search (DFS) and Breadth-First Search (BFS). Let’s take a closer look at each of them.

2 min readMay 30, 2023

DFS explores a graph by traversing as far as possible along each branch before backtracking.
It starts at a source vertex, visits the vertex, and then recursively explores its unvisited neighbors.
DFS uses a stack (either implicitly via recursion or explicitly) to keep track of the vertices to visit.
This algorithm is well-suited for tasks such as finding connected components, cycle detection, and topological sorting.

BFS explores a graph by visiting all the vertices at the same level before moving to the next level.
It starts at a source vertex, visits the vertex, and then explores all of its neighbors before moving to the next level.
BFS uses a queue to keep track of the vertices to visit.
This algorithm is commonly used for finding the shortest path between two vertices, computing connected components, and exploring a graph layer by layer.

Both DFS and BFS have their own strengths and applications, and the choice of which one to use depends on the problem at hand.

Some scenarios where one algorithm might be preferred over the other include:

DFS is often used when we want to explore a graph deeply and efficiently, especially in scenarios such as finding connected components, checking for cycles, or performing topological sorting. It is particularly useful in graphs with a large branching factor or when memory consumption is a concern.
BFS is commonly used when we need to find the shortest path between two vertices or explore a graph in a systematic and level-by-level manner. It guarantees the shortest path in an unweighted graph and can be useful in scenarios like social network analysis, web crawling, or finding the nearest neighbors.