Which adjacency matrix below corresponds to the following graph: 2 4

Please choose the correct answer

Pls Use Python Using NumPy, write the program that determines whether the A=({{1, 5, -2}, {1, 2, -1}, {3, 6, -3}}) matrix is ​​nilpotent.  Itro: Nilpotent Matrix: A square matrix A is called nilpotent matrix of order k provided it satisfies the relation, Ak = O and Ak-1≠O where k is a positive integer & O is a null matrix of order k and k is the order of the nilpotent matrix A . The following picture is an example of the intro:   Ps: Please also explain step by step with " # "

In the following G, each adjacency list is sorted in increasing alphabetical order. B H Draw the component graph GSCC of G.

Q23. * The following Adjacency Matrix represents 1 2 3 4 5 6 1010000 2 1 0 1 1 0 0 3 0 1 0 0 1 0 4 0 1 0 0 1 0 5 0 0 1 1 0 1 0 0 0 1 0 6 0 Undirected Unweighted Graph Undirected Weighted Graph O Directed Unweighted Graph O Directed Weighted Graph

Mark Zuckerberg, the CEO of Facebook, has hired you to lead the Facebook Algorithms Group. He has asked you to use various graph algorithms to analyze the world's largest social network. The Facebook Graph has 2.8 billion vertices, with each vertex being a Facebook user. Two vertices are connected provided those two users are "friends". The first decision you need to make is how you want to model the Facebook graph. Determine whether you should use an adjacency-list representation or an adjacency-matrix representation.

Wouldyou use the adjacency list structure or the adjacency matrix Strijc each of the following cases? Justify your choice. a. The graph has 10,000 vertices and 20,000 edges, and it is importan as little space as possible. b; The graph has 10,000 vertices and 20,000,000 edges, and it is impo use as little space as possible. c. You need to answer the query arëAdjacent as fast as possible, no how much space you use.

: |: Section 3 - Random Forests Decision Trees on their own are effectiev classifiers. The true power, though, comes from a forest of trees -- multiple decision trees working together as an ensemble learner. We create multiple trees, each using a subset of the available attributes, let them each make a guess at the correct classification, and then take the majority vote as the predicted class. Sounds tricky, right? Once again, sklearn to the rescue. We are going to see if a random forest can improve on our decision tree accuracy for the bank data. To begin, we are going to create a Random Forest using the wheat training and test data from the previous section. First, reload our data. 1 # reload the wheat dataset from UCI 2 3 df = pd. read_csv ("seeds_dataset.txt", sep='\\t', engine='python') 4 6 7 df.columns = ['a', 'p', 'compactness', 'length', 'width', 'coeff', 'length_g', 'type'] print (f'Our data has {df.shape [0]} rows and {df.shape [1]} columns') 8 #Mark 70% of the data for…

Consider the following edge-weighted digraph with 8 vertices and 13 edges. V->W weight A->E 1 A->B B->C C->F 4 C->D D->G F->E 39 23 F->B 26 F->A 16 G->F G->C 40 34 H->D 95 H->G 17 Here is a graphical representation of the same edge-weighted digraph: (A) -3---->(B) -39-->(D) 16 95 (E)- -23- (F). 40 (G) (H) Suppose that you run the Bellman-Ford algorithm to compute the shortest paths from H to every other vertex. What is the distTo[] array immediately after the end of three passes of the algorithm (pass 1, 2, and 3)? Each pass consists of relaxing the 13 edges in the order given above. Here is the distTo[] array before the beginning of pass 1: v A B CDE F H. distTo(v] Answer Your answer should be a sequence of 8 integers, separated by whitespace,

The course prerequisites for a computer science major at a local college are numbered as follows: 111 is required for 112 and 210; 112 is required for 312, 313, 209, and 211; and 210 is required for 312. Draw a directed graph that represents this numbering structure.