# Difference Between Depth First Search And Breadth First Search Pdf

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BFS stands for Breadth First Search is a vertex based technique for finding a shortest path in graph. It uses a Queue data structure which follows first in first out. In BFS, one vertex is selected at a time when it is visited and marked then its adjacent are visited and stored in the queue.

## BFS vs DFS: Know the Difference

Depth First search DFS is an algorithm for traversing or searching tree or graph data structures. The algorithm starts at the root top node of a tree and goes as far as it can down a given branch path , then backtracks until it finds an unexplored path, and then explores it. The algorithm does this until the entire graph has been explored. Hopcroft-Karp, tree-traversal and matching algorithm are examples of algorithm that use DFS to find a matching in a graph. Breadth First search BFS is an algorithm for traversing or searching tree or graph data structures. It starts at the tree root and explores all the neighbor nodes at the present depth prior to moving on to the nodes at the next depth level.

Such traversals are classified by the order in which the nodes are visited. The following algorithms are described for a binary tree , but they may be generalized to other trees as well. Unlike linked lists , one-dimensional arrays and other linear data structures , which are canonically traversed in linear order, trees may be traversed in multiple ways. They may be traversed in depth-first or breadth-first order. There are three common ways to traverse them in depth-first order: in-order, pre-order and post-order. The latter, as well as breadth-first search, can also be used to traverse infinite trees, see below. Traversing a tree involves iterating over all nodes in some manner.

The major difference between BFS and DFS is that BFS proceeds level by level while DFS follows first a path form the starting to the ending node vertex , then another path from the start to end, and so on until all nodes are visited. Graph traversal is the process of visiting all the nodes of the graph. Vertices along the edge are explored in the beginning. Optimality Optimal for finding the shortest distance, not in cost. Not optimal Application Examines bipartite graph, connected component and shortest path present in a graph. Examines two-edge connected graph, strongly connected graph, acyclic graph and topological order. It uses a queue for storing the visited vertices.

We also saw another kind of traversal, topological ordering , when I talked about shortest paths. Both of these construct spanning trees with certain properties useful in other graph algorithms. We'll start by describing them in undirected graphs, but they are both also very useful for directed graphs. Let's prove some basic facts about this algorithm. First, each vertex is clearly marked at most once, added to the list at most once since that happens only when it's marked , and therefore removed from the list at most once. Since the time to process a vertex is proportional to the length of its adjacency list, the total time for the whole algorithm is O m. Next, let's look at the tree T constructed by the algorithm.

The depth-first search is an organized graph traversal that recursively visit all of V's unvisited neighbors whenever a V vertex is visited (Clifford, ). THE BREADTH-FIRST SEARCH The breadth-first search is a graph traversal algorithm.

## BFS vs DFS: Know the Difference

Join Stack Overflow to learn, share knowledge, and build your career. Connect and share knowledge within a single location that is structured and easy to search. Backtracking is a more general purpose algorithm. Depth-First search is a specific form of backtracking related to searching tree structures.

Why is depth-first search claimed to be space efficient? It uses a Queue data structure which follows first in first out. In BFS, one vertex is selected at a time when it is visited and marked then its adjacent are visited and stored in the queue.

BFS is an algorithm that is used to graph data or searching tree or traversing structures. The algorithm efficiently visits and marks all the key nodes in a graph in an accurate breadthwise fashion.

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