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The primary distinction between BFS & DFS is that BFS goes in stages, but DFS takes a path from the start to an end node (vertex), then another path from the beginning to the end, and so forth until every node is visited. Furthermore, BFS stores nodes in a queue, whereas DFS traverses nodes in a stack. BFS and DFS are the traversing methods used in graph research. Graph traversal is the Process of viewing all of the nodes within a graph. A graph comprises Processes that can be initiated ‘V’ and Corners ‘E’ that connect the vertices.
What is BFS?
BFS is a method for graphing data, search trees, and traversing structures. The method visits & marks all critical nodes in a network in an exact breadthwise manner. This algorithm chooses a single node (the beginning or origin point) in a network and visits all nodes near the chosen node. After visiting and marking the beginning node, the algorithm goes on to the next uninhabited nodes it analyses them. All nodes are indicated once they have been visited. These cycles continue until all nodes in the graph have been visited and marked correctly. BFS is an abbreviation for Breadth-first search.
What is DFS?
DFS is a depth-first search strategy that may be used to locate or explore graphs or trees. Before backtracking, the algorithm begins at the tree’s root and explores each path. When an iteration reaches a dead end, a stack information structure is utilised to remember, identify the next vertex, and begin a search. DFS’s complete form is a depth-first search.
BFS vs DFS
BFS (Breadth-first search) is a network search method that investigates all surrounding nodes after starting at the root node. DFS (Depth initial search) is an algorithm that starts with the first node in the network and continues to explore until it discovers the desired node or even a node with no children. As a result, the major contrast between BFS and DFS is this.
Difference Between BFS and DFS
| S.no | BFS | DFS |
| Abbreviation for | BFS is an abbreviation of Breadth First Search. | DFS is an abbreviation of Depth First Search. |
| Data Organization | BFS (Breadth First Search) finds the shortest path using the Queue data structure. | DFS (Depth First Search) makes use of the Stack data structure. |
| Technique | Because BFS reaches a vertex with the fewest edges from the source vertex, it may be used to identify a single origin shortest path inside an unweighted graph. | In DFS, we may need to traverse more edges to go from a source vertex to a destination vertex. |
| The difference in Concepts | BFS constructs a tree level per level. | DFS constructs the tree subtree by subtree. |
| Utilised strategy | It is based on the FIFO principle (First in, First Out). | It operates on the LIFO principle (Last in First Out). |
| Appropriate for | BFS is more suited for finding vertices that are near the provided source. | If there are alternatives away from the source, DFS is more appropriate. |
| Suitable for Making a Decision | Treestheirwinning BFS prioritises all neighbours and is hence unsuitable for decision-making trees in games or puzzles. | DFS is more suited to gaming or puzzle challenges. We make a choice and then investigate all possible outcomes. If this decision results in a win-win situation, we cease. |
| Time Complicacy | The running time of BFS becomes O(V E) when using an Adjacency List and O(V2) when using an Adjacency Matrix, where V represents vertices and E represents edges. | DFS has a time complexity of O(V E) when using an Adjacency List and O(V2) when using an Adjacency Matrix, where V refers to vertices & E stands for edges. |
| Traversed Node Removal | Nodes that have been traversed multiple times are removed from the queue. | The seen sites are added to a stack whenever no more sites are visited and subsequently deleted. |
| Applications | BFS is utilised in various applications, including bipartite graphs, shortest routes, etc. | DFS is utilised in various applications, including acyclic graphs & topological order. |
| The complexity of space | In BFS, case complexity is more important than time complexity. | DFS requires less space since it only has to keep a single route from the root to a leaf node at a time. |
| When should you utilise it? | BFS works better whenever the target is near the source. | DFS is advantageous whenever the target is remote from the source. |
| Speed | When compared to DFS, BFS is slower. | When compared to BFS, DFS is faster |
