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In this section we will see what is a graph data structure, and the traversal algorithms of it.

The graph is one non-linear data structure. That is consists of some nodes and their connected edges. The edges may be director or undirected. This graph can be represented as G(V, E). The following graph can be represented as G({A, B, C, D, E}, {(A, B), (B, D), (D, E), (B, C), (C, A)})

The graph has two types of traversal algorithms. These are called the Breadth First Search and Depth First Search.

The Breadth First Search (BFS) traversal is an algorithm, which is used to visit all of the nodes of a given graph. In this traversal algorithm one node is selected and then all of the adjacent nodes are visited one by one. After completing all of the adjacent vertices, it moves further to check another vertices and checks its adjacent vertices again.

bfs(vertices, start) Input: The list of vertices, and the start vertex. Output: Traverse all of the nodes, if the graph is connected. Begin define an empty queue que at first mark all nodes status as unvisited add the start vertex into the que while que is not empty, do delete item from que and set to u display the vertex u for all vertices 1 adjacent with u, do if vertices[i] is unvisited, then mark vertices[i] as temporarily visited add v into the queue mark done mark u as completely visited done End

The Depth First Search (DFS) is a graph traversal algorithm. In this algorithm one starting vertex is given, and when an adjacent vertex is found, it moves to that adjacent vertex first and try to traverse in the same manner.

dfs(vertices, start) Input: The list of all vertices, and the start node. Output: Traverse all nodes in the graph. Begin initially make the state to unvisited for all nodes push start into the stack while stack is not empty, do pop element from stack and set to u display the node u if u is not visited, then mark u as visited for all nodes i connected to u, do if ith vertex is unvisited, then push ith vertex into the stack mark ith vertex as visited done done End

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