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Question9 Show the order in which the nodes of the graph below above are visited by DFS starting at node 3. Assume the use of a stack for depth-first search (DFS). Show the order the nodes will be visited during the search. When pushing neighbours onto the stack, always push them in the order largest to smallest. Type your answer in the following format: 1-2-3 (i.e., node, separated by a dash). The order of nodes: [input] Maximum marks: 3 Flag question undefined
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The task asks to show the DFS visitation order starting at node 3, using a stack and pushing neighbours in descending order. We don’t have the graph’s adjacency list here, so we can’t definitively verify every step, but we can explain how such an order would be produced and what each part implies.
Consider the general DFS-on-stack procedure under the given rule: start at the starting node, mark it visited, push its neighbours in descending order onto the stack, then repeatedly pop the top vertex, visit it if not yet visited, and push that vertex’s unvisited neighbo......Login to view full explanationLog in for full answers
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Similar Questions
The diagram below represents a problem using a search tree. Which of the following options shows the correct order of node visits to reach Wendy using Depth-First Search (DFS)?
How does depth-first search complete its search of the search tree?
DFS_Graph_4 Context: This question pertains to the use of the Graph Abstract Data Type (ADT) implemented with an Adjacency Map, as studied in our course. Instructions: Begin the traversal at Vertex 'A'. When selecting the next vertex to visit, adhere to alphabetical order. Question: Complete the depth-first search (DFS) for the graph shown below. Guidelines: Initiate the traversal at Vertex 'A', and proceed with the exploration, selecting vertices in alphabetical order where multiple paths are available. def DFS(g, u, discovered): for e in g.incident_edges(u): v = e.opposite(u) if v not in discovered: discovered[v] = e # mark v as discovered via edge e DFS(g, v, discovered)
DFS_Pse_2 This question pertains to the use of the Graph Abstract Data Type (ADT) implemented using an adjacency map, as studied in our course. The algorithms DFS and BFS are used to explore graphs but follow different strategies for traversal. Below is a simplified pseudocode version of a Depth-First Search (DFS) algorithm that uses recursion and a discovereddictionary to track visited vertices: DFS(Graph G, Vertex u, Map discovered): for each edge e incident to u in G: let v be the vertex opposite u on edge e if v is not in discovered: discovered[v] ← e // edge e discovered v DFS(G, v, discovered) In the DFS pseudocode, what does the discovered[v] ← e assignment represent? Graph ADT For reference: class Vertex: def __init__(self, x): self._element = x class Edge: def __init__(self, u, v, x): self._origin = u self._destination = v self._element = x def opposite(self, v): return self._destination if v == self._origin else self._origin class Graph: def __init__(self, directed=False): self._outgoing = {} self._incoming = {} if directed else self._outgoing def incident_edges(self, v): return self._outgoing[v].values()
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