Graph traversal techniques explore all vertices in a graph from "summary" of Data Structures and Algorithms in Python by Michael T. Goodrich,Roberto Tamassia,Michael H. Goldwasser
Graph traversal techniques are algorithms that systematically visit the vertices and edges of a graph. These techniques are used to explore all vertices in a graph by traversing through them in a specific manner. One common way to traverse a graph is to start at a given vertex and visit all of its neighbors before moving on to its neighbors' neighbors. This process continues until all vertices in the graph have been visited. There are two main types of graph traversal techniques: depth-first search (DFS) and breadth-first search (BFS). In DFS, the algorithm explores as far as possible along each branch before backtracking. This means that it goes deep into the graph before exploring other branches. On the other hand, BFS explores all the vertices at the present depth before moving on to the vertices at the next depth. This results in a level-by-level exploration of the graph. Both DFS and BFS ensure that all vertices in a graph are visited. However, they differ in their traversal order and the data structures they use to keep track of visited vertices. DFS uses a stack to keep track of vertices to visit, while BFS uses a queue. These data structures help in maintaining the order in which vertices are visited and ensure that each vertex is visited exactly once.- It is possible to systematically explore all vertices in a graph and perform various operations on them. These operations may include finding paths between vertices, detecting cycles, and determining the connectivity of the graph. Overall, graph traversal techniques are essential tools for analyzing and understanding the structure of a graph.
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