Traverse the Directed Acyclic Graph shown in and return the array of nodes where each node appears before all the nodes it points to and describe the routine for the same type of sorting.
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- Let G be an undirected graph whose vertices are the integers 1 through 8, and let the adjacent vertices of each vertex be given by the table below: look at the picture sent Assume that, in a traversal of G, the adjacent vertices of a given vertex are returned in the same order as they are listed in the table above. Which statement of the following is correct? group of answer choices a) The sequence of vertices visited using a DFS traversal starting at vertex 1: 1, 2, 3, 4, 6, 5, 7, 8. b) The sequence of vertices visited using a BFS traversal starting at vertex 1: 1, 2, 3, 4, 6, 5, 7, 8. c) Both sequences are wrong. d) Both sequences are correct.When we learn about Graph Traversals, one question that I'm sometimes asked by students is why we need them at all. Consider the two implementation strategies for graphs that we learned about previously: an adjacency matrix and adjacency lists. Both of them include a separate array-based structure in which information about every vertex is stored. So if our only goal is to visit every vertex, we can do that by just iterating through that array-based structure. If visiting every vertex is as easy as iterating through them, then why do we need graph traversal algorithms such as depth-first and breadth-first? What purpose do they serve that just iterating through the vertices one at a time, without regard for the presence of edges, wouldn't? JAVA PROGRAMMINGLet G be a graph with 7 vertices, where eachvertex is labelled by a number from 1 to 7. Twovertices are selected at random. Let us call themu and v. Now, directed edges are drawn from u toall other vertices except v and directed edges aredrawn from all vertices to v except from u. Let xbe the total possible topological sortings of G.hint: You may remember the terms factorials orfibonacci?Write down the value of x:
- Provide a class to assess DAGs for mathematical expressions. Use a vertex-indexed array to store values for every vertex. Suppose that values for leaves have already been determined. Explain a family of mathematical expressions where the expression tree's size is exponentially bigger than the matching DAG's size (so the running time of your programme for the DAG is proportional to the logarithm of the running time for the tree).COM. Given a graph G and a vertex x of G, present an O(m+n) time algorithm that uses DFS to compute and return the number of vertices that are reachable from x in G via a path; x is reachable from itself. Present a complete algorithmAn Unfair AlgorithmFinding the MWM of a graph is as easy as selecting edges from a list in order, including the edge in matching, and removing all neighbouring edges from the list. This is done by sorting edges according to their weights in a non-increasing sequence. 1. Input: A weighted graph G = (V, E, w)2. Output: A maximal weighted matching M of G 3. M ← Ø 4. sort E and store the list in Q 5. while Q = Ø6. remove first element e from Q7. M ← M ∪ e8. Q ← Q\ { all adjacent edges to e }Let's attempt to use the preceding methods to create this algorithm in Python.
- The Algorithm of AlgebraThe adjacency matrix A of a graph G = is used by the algebraic BFS algorithm (V, E).First, we create a vector x with all zeros except for the index of the source vertex s that we wish to use as the starting point for the algorithm; next, we create the matrix A = A+ I; last, AT x chooses all nodes that are at a distance of 1 (level 1) from the source vertex. The vertices with the fewest number of hops are obtained by multiplying the vector x by the matrix A2. As a rule, the product Ak x will produce neighbours that are at most k hops distant, and the multiplication should be done in a boolean manner as in the algorithm shown below. Algorithm for Algebraic BFS1: Input : Adjacency matrix An,n of a graph G = (V, E) connected, unweighted graph G and asource vertex s2: Output : N, visited, levels a matrix that shows level i vertices at its column i, the visitedvertices in sequence and their levels3: x[n] ← 04: x[s] ← 15: A ← A + I6: for i = 1 to n do7: N ← AT · x8:…The algorithm for finding connected components in a graph is applied to eachof the three graphs. The output components are of different sizes, so split andmerge routines are applied to achieve a better partition to patches.Draw a tree with 14 vertices Draw a directed acyclic graph with 6 vertices and 14 edges Suppose that your computer only has enough memory to store 40000 entries. Which best graph data structure(s) – you can choose more than 1 -- should you use to store a simple undirected graph with 200 vertices, 19900 edges, and the existence of edge(u,v) is frequently asked? - Adjacency Matrix - Adjacency List - Edge List
- Draw a graph with the following conditions: 12 nodes total Directed, Acyclic Would have 4 possible options on the first iteration of topological sort5. (This question goes slightly beyond what was covered in the lectures, but you can solve it by combining algorithms that we have described.) A directed graph is said to be strongly connected if every vertex is reachable from every other vertex; i.e., for every pair of vertices u, v, there is a directed path from u to v and a directed path from v to u. A strong component of a graph is then a maximal subgraph that is strongly connected. That is all vertices in a strong component can reach each other, and any other vertex in the directed graph either cannot reach the strong component or cannot be reached from the component. (Note that we are considering directed graphs, so for a pair of vertices u and v there could be a path from u to v, but no path path from v back to u; in that case, u and v are not in the same strong component, even though they are connected by a path in one direction.) Given a vertex v in a directed graph D, design an algorithm for com- puting the strong connected…Write algorithm LargestShortestWeight(G, s,t) pre-cond: G is a weighted directed (augmenting) graph. s is the sourcenode.t is the sink. post-cond: P specifies a path from s to t whose smallest edge weight is aslarge as possible. u, v is its smallest weighted edge.