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DFS and BFS are both searching algorithms.

DFS, or depth first search, is a simple to implement algorithm, especially when written recursively.

BFS, or breadth first search, is only slightly more complicated.

Both search methods can be used to obtain a spanning tree of the graph, though if I recall correctly, BFS can also be used in a weighted graph to generate a minimum cost spanning tree.

Q: Why you use DFS and BFS in graphs?

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dfs better then from bfs..

It isn't necessarily better... They each have their own pros and cons

BFS: This can be throught of as being like Dijkstra's algorithm for shortest paths, but with every edge having the same length. However it is a lot simpler and doesn't need any data structures. We just keep a tree (the breadth first search tree), a list of nodes to be added to the tree, and markings (Boolean variables) on the vertices to tell whether they are in the tree or list. Depth first search is another way of traversing graphs, which is closely related to preorder traversal of a tree. Recall that preorder traversal simply visits each node before its children. It is most easy to program as a recursive routine:

Stand-alone DFS Namespace In a stand-alone DFS namespace, the path to access the root or a link starts with the root server name. The stand-alone DFS root can comprise of a single root target. Therefore, these are not fault tolerant. When the root target is not available, you cannot access the complete DFS namespace. You can enable fault tolerance on a stand-alone DFS namespace by creating these namespaces on a cluster of servers. A stand-alone DFS namespace has its configuration information stored in the local registry of eth root server. Domain-based DFS Namespace In a domain-based DFS namespace, the path to access the root or a link starts with the domain name of the host. The domain-based DFS root can comprise of single or multiple root targets that enables fault tolerance and load sharing. A domain-based DFS namespace has its configuration information stored in the Active Directory. Exemple : â€¢ \\DomainName\RootName: This is the format of the namespace when you select the Domain-based DFS namespace type. â€¢ \\ServerName\RootName: This is the format of the namespace when you select the Stand-alone DFS namespace type.

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dfs better then from bfs..

1. bfs uses queue implementation ie.FIFO dfs uses stack implementation ie. LIFO 2. dfs is faster than bfs 3. dfs requires less memory than bfs 4. dfs are used to perform recursive procedures.

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ok here we go...Proof:If the some graph G has the same DFS and BFS then that means that G should not have any cycle(work out for any G with a cycle u will never get the same BFS and DFS .... and for a graph without any cycle u will get the same BFS/DFS).We will prove it by contradiction:So say if T is the tree obtained by BFS/DFS, and let us assume that G has atleast one edge more than T. So one more edge to T(T is a tree) would result in a cycle in G, but according to the above established principle no graph which has a cycle would result the same DFS and BFS, so out assumption is a contradiction.Hence G should have more edges than T, which implies that if the BFS and DFS for a graph G are the same then the G = T.Hope this helps u......................

yes pagal

It isn't necessarily better... They each have their own pros and cons

DFS and BFS stands for Depth First Search and Breadth First Search respectively. In DFS algorithm every node is explored in depth; tracking back upon hitting an already visited node and starts visiting from a node which has any adjacent nodes unvisited. In BFS, the nodes are visited level wise. These algorithms are used to traverse the nodes on a connected digraph. Primal

Use a simple DFS/BFS traversal. If you have gone through all nodes, the graph is connected.

Prove_that_if_a_DFS_and_BFS_trees_in_graph_G_are_the_same_than

DFS, BFS

Pie Graphs, Bar Graphs, and Line Graphs are three graphs that scientist use often.

BFS: This can be throught of as being like Dijkstra's algorithm for shortest paths, but with every edge having the same length. However it is a lot simpler and doesn't need any data structures. We just keep a tree (the breadth first search tree), a list of nodes to be added to the tree, and markings (Boolean variables) on the vertices to tell whether they are in the tree or list. Depth first search is another way of traversing graphs, which is closely related to preorder traversal of a tree. Recall that preorder traversal simply visits each node before its children. It is most easy to program as a recursive routine: