Given a directed graph, find out whether the graph is strongly connected or not. A directed graph is strongly connected if there is a path between any two pair of vertices. For example, following is a strongly connected graph.

**It is easy for undirected graph**, we can just do a BFS and DFS starting from any vertex. If BFS or DFS visits all vertices, then the given undirected graph is connected. This approach won’t work for a directed graph. For example, consider the following graph which is not strongly connected. If we start DFS (or BFS) from vertex 0, we can reach all vertices, but if we start from any other vertex, we cannot reach all vertices.

**How to do for directed graph?**

A simple idea is to use a all pair shortest path algorithm like **Floyd Warshall or find Transitive Closure** of graph. Time complexity of this method would be O(v^{3}).

We can also **do DFS V times** starting from every vertex. If any DFS, doesn’t visit all vertices, then graph is not strongly connected. This algorithm takes O(V*(V+E)) time which can be same as transitive closure for a dense graph.

A better idea can be **Strongly Connected Components (SCC) algorithm**. We can find all SCCs in O(V+E) time. If number of SCCs is one, then graph is strongly connected. The algorithm for SCC does extra work as it finds all SCCs.

Following is **Kosaraju’s DFS based simple algorithm that does two DFS traversals** of graph:

**1)** Initialize all vertices as not visited.

**2)** Do a DFS traversal of graph starting from any arbitrary vertex v. If DFS traversal doesn’t visit all vertices, then return false.

**3)** Reverse all arcs (or find transpose or reverse of graph)

**4)** Mark all vertices as not-visited in reversed graph.

**5)** Do a DFS traversal of reversed graph starting from same vertex v (Same as step 2). If DFS traversal doesn’t visit all vertices, then return false. Otherwise return true.

The idea is, if every node can be reached from a vertex v, and every node can reach v, then the graph is strongly connected. In step 2, we check if all vertices are reachable from v. In step 4, we check if all vertices can reach v (In reversed graph, if all vertices are reachable from v, then all vertices can reach v in original graph).

Following is the implementation of above algorithm.

## C++

// C++ program to check if a given directed graph is strongly // connected or not #include <iostream> #include <list> #include <stack> using namespace std; class Graph { int V; // No. of vertices list<int> *adj; // An array of adjacency lists // A recursive function to print DFS starting from v void DFSUtil(int v, bool visited[]); public: // Constructor and Destructor Graph(int V) { this->V = V; adj = new list<int>[V];} ~Graph() { delete [] adj; } // Method to add an edge void addEdge(int v, int w); // The main function that returns true if the graph is strongly // connected, otherwise false bool isSC(); // Function that returns reverse (or transpose) of this graph Graph getTranspose(); }; // A recursive function to print DFS starting from v void Graph::DFSUtil(int v, bool visited[]) { // Mark the current node as visited and print it visited[v] = true; // Recur for all the vertices adjacent to this vertex list<int>::iterator i; for (i = adj[v].begin(); i != adj[v].end(); ++i) if (!visited[*i]) DFSUtil(*i, visited); } // Function that returns reverse (or transpose) of this graph Graph Graph::getTranspose() { Graph g(V); for (int v = 0; v < V; v++) { // Recur for all the vertices adjacent to this vertex list<int>::iterator i; for(i = adj[v].begin(); i != adj[v].end(); ++i) { g.adj[*i].push_back(v); } } return g; } void Graph::addEdge(int v, int w) { adj[v].push_back(w); // Add w to v’s list. } // The main function that returns true if graph is strongly connected bool Graph::isSC() { // St1p 1: Mark all the vertices as not visited (For first DFS) bool visited[V]; for (int i = 0; i < V; i++) visited[i] = false; // Step 2: Do DFS traversal starting from first vertex. DFSUtil(0, visited); // If DFS traversal doesn’t visit all vertices, then return false. for (int i = 0; i < V; i++) if (visited[i] == false) return false; // Step 3: Create a reversed graph Graph gr = getTranspose(); // Step 4: Mark all the vertices as not visited (For second DFS) for(int i = 0; i < V; i++) visited[i] = false; // Step 5: Do DFS for reversed graph starting from first vertex. // Staring Vertex must be same starting point of first DFS gr.DFSUtil(0, visited); // If all vertices are not visited in second DFS, then // return false for (int i = 0; i < V; i++) if (visited[i] == false) return false; return true; } // Driver program to test above functions int main() { // Create graphs given in the above diagrams Graph g1(5); g1.addEdge(0, 1); g1.addEdge(1, 2); g1.addEdge(2, 3); g1.addEdge(3, 0); g1.addEdge(2, 4); g1.addEdge(4, 2); g1.isSC()? cout << "Yes\n" : cout << "No\n"; Graph g2(4); g2.addEdge(0, 1); g2.addEdge(1, 2); g2.addEdge(2, 3); g2.isSC()? cout << "Yes\n" : cout << "No\n"; return 0; }

## Java

// Java program to check if a given directed graph is strongly // connected or not import java.io.*; import java.util.*; import java.util.LinkedList; // This class represents a directed graph using adjacency // list representation class Graph { private int V; // No. of vertices private LinkedList<Integer> adj[]; //Adjacency List //Constructor Graph(int v) { V = v; adj = new LinkedList[v]; for (int i=0; i<v; ++i) adj[i] = new LinkedList(); } //Function to add an edge into the graph void addEdge(int v,int w) { adj[v].add(w); } // A recursive function to print DFS starting from v void DFSUtil(int v,Boolean visited[]) { // Mark the current node as visited and print it visited[v] = true; int n; // Recur for all the vertices adjacent to this vertex Iterator<Integer> i = adj[v].iterator(); while (i.hasNext()) { n = i.next(); if (!visited[n]) DFSUtil(n,visited); } } // Function that returns transpose of this graph Graph getTranspose() { Graph g = new Graph(V); for (int v = 0; v < V; v++) { // Recur for all the vertices adjacent to this vertex Iterator<Integer> i = adj[v].listIterator(); while (i.hasNext()) g.adj[i.next()].add(v); } return g; } // The main function that returns true if graph is strongly // connected Boolean isSC() { // Step 1: Mark all the vertices as not visited // (For first DFS) Boolean visited[] = new Boolean[V]; for (int i = 0; i < V; i++) visited[i] = false; // Step 2: Do DFS traversal starting from first vertex. DFSUtil(0, visited); // If DFS traversal doesn't visit all vertices, then // return false. for (int i = 0; i < V; i++) if (visited[i] == false) return false; // Step 3: Create a reversed graph Graph gr = getTranspose(); // Step 4: Mark all the vertices as not visited (For // second DFS) for (int i = 0; i < V; i++) visited[i] = false; // Step 5: Do DFS for reversed graph starting from // first vertex. Staring Vertex must be same starting // point of first DFS gr.DFSUtil(0, visited); // If all vertices are not visited in second DFS, then // return false for (int i = 0; i < V; i++) if (visited[i] == false) return false; return true; } public static void main(String args[]) { // Create graphs given in the above diagrams Graph g1 = new Graph(5); g1.addEdge(0, 1); g1.addEdge(1, 2); g1.addEdge(2, 3); g1.addEdge(3, 0); g1.addEdge(2, 4); g1.addEdge(4, 2); if (g1.isSC()) System.out.println("Yes"); else System.out.println("No"); Graph g2 = new Graph(4); g2.addEdge(0, 1); g2.addEdge(1, 2); g2.addEdge(2, 3); if (g2.isSC()) System.out.println("Yes"); else System.out.println("No"); } } // This code is contributed by Aakash Hasija

## Python

# Python program to check if a given directed graph is strongly # connected or not from collections import defaultdict #This class represents a directed graph using adjacency list representation class Graph: def __init__(self,vertices): self.V= vertices #No. of vertices self.graph = defaultdict(list) # default dictionary to store graph # function to add an edge to graph def addEdge(self,u,v): self.graph[u].append(v) #A function used by isSC() to perform DFS def DFSUtil(self,v,visited): # Mark the current node as visited visited[v]= True #Recur for all the vertices adjacent to this vertex for i in self.graph[v]: if visited[i]==False: self.DFSUtil(i,visited) # Function that returns reverse (or transpose) of this graph def getTranspose(self): g = Graph(self.V) # Recur for all the vertices adjacent to this vertex for i in self.graph: for j in self.graph[i]: g.addEdge(j,i) return g # The main function that returns true if graph is strongly connected def isSC(self): # Step 1: Mark all the vertices as not visited (For first DFS) visited =[False]*(self.V) # Step 2: Do DFS traversal starting from first vertex. self.DFSUtil(0,visited) # If DFS traversal doesnt visit all vertices, then return false if any(i == False for i in visited): return False # Step 3: Create a reversed graph gr = self.getTranspose() # Step 4: Mark all the vertices as not visited (For second DFS) visited =[False]*(self.V) # Step 5: Do DFS for reversed graph starting from first vertex. # Staring Vertex must be same starting point of first DFS gr.DFSUtil(0,visited) # If all vertices are not visited in second DFS, then # return false if any(i == False for i in visited): return False return True # Create a graph given in the above diagram g1 = Graph(5) g1.addEdge(0, 1) g1.addEdge(1, 2) g1.addEdge(2, 3) g1.addEdge(3, 0) g1.addEdge(2, 4) g1.addEdge(4, 2) print "Yes" if g1.isSC() else "No" g2 = Graph(4) g2.addEdge(0, 1) g2.addEdge(1, 2) g2.addEdge(2, 3) print "Yes" if g2.isSC() else "No" #This code is contributed by Neelam Yadav

Output:

Yes No

**Time Complexity:** Time complexity of above implementation is sane as Depth First Search which is O(V+E) if the graph is represented using adjacency list representation.

**Can we improve further?**

The above approach requires two traversals of graph. We can find whether a graph is strongly connected or not in one traversal using Tarjan’s Algorithm to find Strongly Connected Components.

**Exercise:**

Can we use BFS instead of DFS in above algorithm? See this.

**References:**

http://www.ieor.berkeley.edu/~hochbaum/files/ieor266-2012.pdf

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