Which Data Structure Is Used for BFS of a Graph?


Angela Bailey

Which Data Structure Is Used for BFS of a Graph?

Breadth-First Search (BFS) is a popular graph traversal algorithm that explores all the vertices of a graph in breadth-first order. It starts at a given source vertex and visits all the neighboring vertices before moving on to the next level of vertices.

To efficiently implement BFS, we need to choose the appropriate data structure(s) to store and manage the vertices and edges during the traversal.

Queue: The Key Data Structure

The most crucial data structure used in BFS is a queue. A queue follows the First-In-First-Out (FIFO) principle, which means that elements are added at one end and removed from the other end.

In the context of BFS, we enqueue each unvisited neighboring vertex while exploring a vertex and dequeue them in order to visit them later.

By using a queue, we can ensure that the vertices are visited in the order they were discovered from the source vertex. This property guarantees that BFS visits all vertices at distance k from the source before visiting any vertex at distance k+1.

Additional Data Structures for Efficient Implementation

Apart from a queue, there are other data structures used to implement BFS efficiently:

  • Visited Array: It is an array that keeps track of visited/unvisited vertices. This array ensures that each vertex is visited only once during traversal.
  • Adjacency List: It represents graph connections by storing each vertex’s adjacent vertices in a linked list or an array. This data structure allows for efficient retrieval of neighboring vertices during traversal.

Algorithmic Steps

Now, let’s summarize the algorithmic steps to perform BFS on a graph using the mentioned data structures:

  1. Initialize an empty queue and a visited array.
  2. Enqueue the source vertex into the queue and mark it as visited.
  3. While the queue is not empty, repeat steps 4-6.
  4. Dequeue a vertex from the queue and process it.
  5. Enqueue all unvisited neighboring vertices of the processed vertex into the queue and mark them as visited.
  6. Repeat step 3 until the queue becomes empty.


In conclusion, BFS is an essential graph traversal algorithm that can be efficiently implemented using a queue as its primary data structure. The use of additional data structures such as a visited array and an adjacency list further enhances its efficiency.

By understanding these underlying data structures, you can effectively apply BFS to solve various graph-related problems.

Happy coding!

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