help Q11Consider the graph G below:аbeCdWhat are the strongly connected components of G?O {a}, {b, c, d, e}O {a, b, e}, {c}, {d}O {a}, {b}, {c}, {d}, {e}O {a, b, c, d}, {e}O {a, b, c, d, e}

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Answered: Consider the graph G below: b a e d…

Consider the graph G below: b a e d What are the strongly connected components of G? O {a}, {b, c, d, e} O {a, b, e}, {c}, {d} O {a}, {b}, {c}, {d}, {e} O {a, b, c, d}, {e} O {a, b, c, d, e}

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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"""1.In a directed graph, a strongly connected component is a set of vertices suchthat for any pairs of vertices u and v there exists a path (u-...-v) thatconnects them. A graph is strongly connected if it is a single stronglyconnected component.""" from collections import defaultdict class Graph: """ A directed graph where edges are one-way (a two-way edge can be represented by using two edges). """ def __init__(self,vertex_count): """ Create a new graph with vertex_count vertices. """ self.vertex_count = vertex_count self.graph = defaultdict(list) def add_edge(self,source,target): """ Add an edge going from source to target """ self.graph[source].append(target) def dfs(self): """ Determine if all nodes are reachable from node 0 """ visited = [False] * self.vertex_count self.dfs_util(0,visited) if visited == [True]*self.vertex_count: return True…
''In graph theory, a component, sometimes called a connected component,of an undirected graph is a subgraph in which anytwo vertices are connected to each other by paths.Example: 1 3------------7 | | 2--------4 | | | | output = 2 6--------5''' # Code is Here def dfs(source,visited,adjacency_list): ''' Function that performs DFS ''' visited[source] = True for child in adjacency_list[source]: if not visited[child]: dfs(child,visited,adjacency_list) def count_components(adjacency_list,size): ''' Function that counts the Connected components on bases of DFS. return type : int ''' count = 0 visited = [False]*(size+1) for i in range(1,size+1): if not visited[i]: dfs(i,visited,adjacency_list) count+=1 return count def main(): """ Example application """ node_count,edge_count = map(int, input("Enter the Number of Nodes and Edges…
Code it. In graph theory, a component, sometimes called a connected component,of an undirected graph is a subgraph in which anytwo vertices are connected to each other by paths.Example: 1 3------------7 | | 2--------4 | | | | output = 2 6--------5''' # Code is Here def dfs(source,visited,adjacency_list): ''' Function that performs DFS ''' visited[source] = True for child in adjacency_list[source]: if not visited[child]: dfs(child,visited,adjacency_list) def count_components(adjacency_list,size): ''' Function that counts the Connected components on bases of DFS. return type : int ''' count = 0 visited = [False]*(size+1) for i in range(1,size+1): if not visited[i]: dfs(i,visited,adjacency_list) count+=1 return count def main(): """ Example application """ node_count,edge_count = map(int, input("Enter the Number of Nodes and Edges…
This assignment requires the extension of your graph code to apply it to movement through a “world”. The world will be a weighted, directed graph, with nodes for the start position and target(s), and other nodes containing blocks, diversions, boosts and portals. For example, in a cat world, a dog may block you, toys may take your attention, food may give you more energy and portals may prove that cats are pan-dimensional beings. This structure could also be used to implement Snakes and Ladders, or other games. Your task is to build a representation of the world and explore the possible routes through the world and rank them. Sample input files will be available – with various scenarios in a cat world. Your program should be called gameofcatz.py/java, and have three starting options: * No command line arguments : provides usage information * "-i" : interactive testing environment * "-s" : simulation mode (usage: gameofcatz –s infile savefile) When the program starts in interactive mode,…
Which of the following graphs are trees? 1. A graph G with V(G)={a,b,c,d,e} and E(G)={{a,b},{a,c},{d,e}} 2. A graph G with V(G)={a,b,c,d,e} and E(G)={{a,b},{a,c},{a,d},{a,e}} 3. A graph G with V(G)={a,b,c,d,e} and E(G)={{a,b},{a,c},{b,c},{c,d},{d,e}} 4. A graph G with V(G)={a,b,c,d,e} and E(G)={{a,b},{a,e},{b,c},{c,d},{d,e}}
Graphs implement the BFS and DFS graph traversal algorithms as a part of the code below (graph.cpp). The code reads in a graph from the sample_edges.txt file (to be put in the same directory as your code), and returns an adjacency list. The adjacency list is a vector, where each element is in turn a vector of (adjacent vertex, edge weight) pairs. Typedef is used to enhance readability. You are to use the adjacency list returned to implement the BFS and DFS algorithms. A header for the helper function is also included to implement DFS recursively. A utility function that prints the queue is provided to help you with the BFS implementation. Note that the BFS an DFS order the vertices are printed could be different from that shown in the test comments. graph.cpp /* Graph read from file, and represnted as adjacency list. To implement DFS and BFS on the graph */ #include <iostream> #include <sstream> #include <fstream> #include <vector> #include…
4-Clique Problem The clique problem is to find cliques in a graph. A clique is a set of vertices that are all adjacent - connected - to each other. A 4-clique is a set of 4 vertices that are all connected to each other. So in this example of the 4-Clique Problem, we have a 7-vertex graph. A brute-force algorithm has searched every possible combination of 4 vertices and found a set that forms a clique: https://en.wikipedia.org/wiki/Clique_problem You should read the Wikipedia page for the Clique Problem (and then read wider if need be) if you need to understand more about it. Note that the Clique Problem is NP-Complete and therefore when the graph size is large a deterministic search is impractical. That makes it an ideal candidate for an evolutionary search. For this assignment you must suppose that you have been tasked to implement the 4-clique problem as an evolutionary algorithm for any graph with any number of vertices (an n-vertex graph). The algorithm succeeds if it finds a…
Assume a graph G is simple (ie. no self loop or parallel edges) .Let v be any vertex in the graph. Let boolean[] marked be initalized to all false. Consider: boolean dfs(Graph G, int v) { marked[v] = true; for (int w : G.adj(v)) { if (w == u) continue; if (marked[w]) return true; if (dfs (G, v, w)) return true; } return false; } If the call dfs(v) returns true, then: a. The graph has cyclesb. The graph is bipartitec. The graph is connected
function [ ] = square_spectrum( L,N )%Activity 1 for CEN415 Summer 2022 x=linspace(0,2*L,200);f1=(-1).^floor(x/L);plot(x,f1)f2=0;for n=1:2:N, f2=f2+4*sin(n*pi.*x/L)/n/pi;endhold onplot(x,f2)hold offend Q1= Which one is true? a. When the N increases, the approximated graph tends to be closer to the original square wave b. When the L increases, the approximated graph tends to be closer to the original square wave c. When the L decreases, the approximated graph tends to be closer to the original square wave d. When the N decreases, the approximated graph tends to be closer to the original square wave e. None
Locate a huge digraph online, such as a transaction graph in an online system or a digraph defined by links on web sites. Create a software called RandomRealDigraph that generates a graph by selecting V vertices and E directed edges at random from the subgraph induced by those vertices.
Which of the following is the size of an adjacency list graph representation? V refers to the number of vertices, E the number of edges. 1.O(V + E) 2.O(V * 2E) 3.O(V - E) 4.O(V * E)
IN C LANGUAGE I HAD AN ERROR. URGENT!! struct Graph* createGraph(int V, int E){ struct Graph* graph = new Graph; graph->V = V; graph->E = E; graph->edge = new Edge[E]; return graph;} // A structure to represent a subset for union-findstruct subset { int parent; int rank;}; // A utility function to find set of an element i// (uses path compression technique)int find(struct subset subsets[], int i){ // find root and make root as parent of i // (path compression) if (subsets[i].parent != i) subsets[i].parent = find(subsets, subsets[i].parent); return subsets[i].parent;} // A function that does the union of two sets of x and yvoid Union(struct subset subsets[], int x, int y){ int xroot = find(subsets, x); int yroot = find(subsets, y); // Attach smaller rank tree under root of high // rank tree (Union by Rank) if…