# What Is Depth First Search in Data Structure?

//

Heather Bennett

Depth First Search (DFS) is a popular algorithm used in data structures to traverse and search through a graph or tree. It explores as far as possible along each branch before backtracking. Let’s dive into the details of DFS and understand its working mechanism.

## How Does Depth First Search Work?

The DFS algorithm starts at a specific node and explores as far as possible along each branch before backtracking.

Step 1: Start by selecting a node from the graph or tree. This node will be the starting point for the traversal.

Step 2: Visit the selected node and mark it as visited. You can use an array, hash table, or any other data structure to keep track of visited nodes.

Step 3: Explore all the adjacent nodes of the current node recursively. For each unvisited adjacent node, repeat steps 2 and 3.

Step 4: If there are no unvisited adjacent nodes, backtrack to the previous node and repeat step 3 until all nodes have been visited.

## Implementation of Depth First Search

To implement DFS in code, you can use various programming languages such as C++, Java, Python, or JavaScript. Here’s an example implementation using Python:

``````
def dfs(graph, start_node):
visited = set()
stack = [start_node]

while stack:
node = stack.pop()
if node not in visited:
stack.extend(graph[node] - visited)

return visited
```
```

## Applications of Depth First Search

The DFS algorithm has various applications in computer science and real-life scenarios:

• Maze Solving: DFS can be used to find a way out of a maze by exploring each possible path until the exit is found.
• Topological Sorting: DFS can be used to perform topological sorting of directed acyclic graphs.
• Detecting Cycles: DFS can detect cycles in a graph or tree by keeping track of visited nodes during the traversal.

• DFS is easy to implement and understand.
• It requires less memory compared to Breadth First Search (BFS) as it explores one branch completely before moving on to the next.