An edge in an undirected connected graph is a bridge iff removing it disconnects the graph. For a disconnected undirected graph, definition is similar, a bridge is an edge removing which increases number of connected components.

Like Articulation Points,bridges represent vulnerabilities in a connected network and are useful for designing reliable networks. For example, in a wired computer network, an articulation point indicates the critical computers and a bridge indicates the critical wires or connections.

Following are some example graphs with bridges highlighted with red color.

**How to find all bridges in a given graph?**

A simple approach is to one by one remove all edges and see if removal of a edge causes disconnected graph. Following are steps of simple approach for connected graph.

1) For every edge (u, v), do following

…..a) Remove (u, v) from graph

..…b) See if the graph remains connected (We can either use BFS or DFS)

…..c) Add (u, v) back to the graph.

Time complexity of above method is O(E*(V+E)) for a graph represented using adjacency list. Can we do better?

**A O(V+E) algorithm to find all Bridges**

The idea is similar to O(V+E) algorithm for Articulation Points. We do DFS traversal of the given graph. In DFS tree an edge (u, v) (u is parent of v in DFS tree) is bridge if there does not exit any other alternative to reach u or an ancestor of u from subtree rooted with v. As discussed in the previous post, the value low[v] indicates earliest visited vertex reachable from subtree rooted with v. *The condition for an edge (u, v) to be a bridge is, “low[v] > disc[u]”*.

Following are C++ and Java implementations of above approach.

## C++

// A C++ program to find bridges in a given undirected graph #include<iostream> #include <list> #define NIL -1 using namespace std; // A class that represents an undirected graph class Graph { int V; // No. of vertices list<int> *adj; // A dynamic array of adjacency lists void bridgeUtil(int v, bool visited[], int disc[], int low[], int parent[]); public: Graph(int V); // Constructor void addEdge(int v, int w); // to add an edge to graph void bridge(); // prints all bridges }; Graph::Graph(int V) { this->V = V; adj = new list<int>[V]; } void Graph::addEdge(int v, int w) { adj[v].push_back(w); adj[w].push_back(v); // Note: the graph is undirected } // A recursive function that finds and prints bridges using // DFS traversal // u --> The vertex to be visited next // visited[] --> keeps tract of visited vertices // disc[] --> Stores discovery times of visited vertices // parent[] --> Stores parent vertices in DFS tree void Graph::bridgeUtil(int u, bool visited[], int disc[], int low[], int parent[]) { // A static variable is used for simplicity, we can // avoid use of static variable by passing a pointer. static int time = 0; // Mark the current node as visited visited[u] = true; // Initialize discovery time and low value disc[u] = low[u] = ++time; // Go through all vertices aadjacent to this list<int>::iterator i; for (i = adj[u].begin(); i != adj[u].end(); ++i) { int v = *i; // v is current adjacent of u // If v is not visited yet, then recur for it if (!visited[v]) { parent[v] = u; bridgeUtil(v, visited, disc, low, parent); // Check if the subtree rooted with v has a // connection to one of the ancestors of u low[u] = min(low[u], low[v]); // If the lowest vertex reachable from subtree // under v is below u in DFS tree, then u-v // is a bridge if (low[v] > disc[u]) cout << u <<" " << v << endl; } // Update low value of u for parent function calls. else if (v != parent[u]) low[u] = min(low[u], disc[v]); } } // DFS based function to find all bridges. It uses recursive // function bridgeUtil() void Graph::bridge() { // Mark all the vertices as not visited bool *visited = new bool[V]; int *disc = new int[V]; int *low = new int[V]; int *parent = new int[V]; // Initialize parent and visited arrays for (int i = 0; i < V; i++) { parent[i] = NIL; visited[i] = false; } // Call the recursive helper function to find Bridges // in DFS tree rooted with vertex 'i' for (int i = 0; i < V; i++) if (visited[i] == false) bridgeUtil(i, visited, disc, low, parent); } // Driver program to test above function int main() { // Create graphs given in above diagrams cout << "\nBridges in first graph \n"; Graph g1(5); g1.addEdge(1, 0); g1.addEdge(0, 2); g1.addEdge(2, 1); g1.addEdge(0, 3); g1.addEdge(3, 4); g1.bridge(); cout << "\nBridges in second graph \n"; Graph g2(4); g2.addEdge(0, 1); g2.addEdge(1, 2); g2.addEdge(2, 3); g2.bridge(); cout << "\nBridges in third graph \n"; Graph g3(7); g3.addEdge(0, 1); g3.addEdge(1, 2); g3.addEdge(2, 0); g3.addEdge(1, 3); g3.addEdge(1, 4); g3.addEdge(1, 6); g3.addEdge(3, 5); g3.addEdge(4, 5); g3.bridge(); return 0; }

## Java

// A Java program to find bridges in a given undirected graph import java.io.*; import java.util.*; import java.util.LinkedList; // This class represents a undirected graph using adjacency list // representation class Graph { private int V; // No. of vertices // Array of lists for Adjacency List Representation private LinkedList<Integer> adj[]; int time = 0; static final int NIL = -1; // 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); // Add w to v's list. adj[w].add(v); //Add v to w's list } // A recursive function that finds and prints bridges // using DFS traversal // u --> The vertex to be visited next // visited[] --> keeps tract of visited vertices // disc[] --> Stores discovery times of visited vertices // parent[] --> Stores parent vertices in DFS tree void bridgeUtil(int u, boolean visited[], int disc[], int low[], int parent[]) { // Count of children in DFS Tree int children = 0; // Mark the current node as visited visited[u] = true; // Initialize discovery time and low value disc[u] = low[u] = ++time; // Go through all vertices aadjacent to this Iterator<Integer> i = adj[u].iterator(); while (i.hasNext()) { int v = i.next(); // v is current adjacent of u // If v is not visited yet, then make it a child // of u in DFS tree and recur for it. // If v is not visited yet, then recur for it if (!visited[v]) { parent[v] = u; bridgeUtil(v, visited, disc, low, parent); // Check if the subtree rooted with v has a // connection to one of the ancestors of u low[u] = Math.min(low[u], low[v]); // If the lowest vertex reachable from subtree // under v is below u in DFS tree, then u-v is // a bridge if (low[v] > disc[u]) System.out.println(u+" "+v); } // Update low value of u for parent function calls. else if (v != parent[u]) low[u] = Math.min(low[u], disc[v]); } } // DFS based function to find all bridges. It uses recursive // function bridgeUtil() void bridge() { // Mark all the vertices as not visited boolean visited[] = new boolean[V]; int disc[] = new int[V]; int low[] = new int[V]; int parent[] = new int[V]; // Initialize parent and visited, and ap(articulation point) // arrays for (int i = 0; i < V; i++) { parent[i] = NIL; visited[i] = false; } // Call the recursive helper function to find Bridges // in DFS tree rooted with vertex 'i' for (int i = 0; i < V; i++) if (visited[i] == false) bridgeUtil(i, visited, disc, low, parent); } public static void main(String args[]) { // Create graphs given in above diagrams System.out.println("Bridges in first graph "); Graph g1 = new Graph(5); g1.addEdge(1, 0); g1.addEdge(0, 2); g1.addEdge(2, 1); g1.addEdge(0, 3); g1.addEdge(3, 4); g1.bridge(); System.out.println(); System.out.println("Bridges in Second graph"); Graph g2 = new Graph(4); g2.addEdge(0, 1); g2.addEdge(1, 2); g2.addEdge(2, 3); g2.bridge(); System.out.println(); System.out.println("Bridges in Third graph "); Graph g3 = new Graph(7); g3.addEdge(0, 1); g3.addEdge(1, 2); g3.addEdge(2, 0); g3.addEdge(1, 3); g3.addEdge(1, 4); g3.addEdge(1, 6); g3.addEdge(3, 5); g3.addEdge(4, 5); g3.bridge(); } } // This code is contributed by Aakash Hasija

## Python

# Python program to find bridges in a given undirected graph #Complexity : O(V+E) from collections import defaultdict #This class represents an undirected 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 self.Time = 0 # function to add an edge to graph def addEdge(self,u,v): self.graph[u].append(v) self.graph[v].append(u) '''A recursive function that finds and prints bridges using DFS traversal u --> The vertex to be visited next visited[] --> keeps tract of visited vertices disc[] --> Stores discovery times of visited vertices parent[] --> Stores parent vertices in DFS tree''' def bridgeUtil(self,u, visited, parent, low, disc): #Count of children in current node children =0 # Mark the current node as visited and print it visited[u]= True # Initialize discovery time and low value disc[u] = self.Time low[u] = self.Time self.Time += 1 #Recur for all the vertices adjacent to this vertex for v in self.graph[u]: # If v is not visited yet, then make it a child of u # in DFS tree and recur for it if visited[v] == False : parent[v] = u children += 1 self.bridgeUtil(v, visited, parent, low, disc) # Check if the subtree rooted with v has a connection to # one of the ancestors of u low[u] = min(low[u], low[v]) ''' If the lowest vertex reachable from subtree under v is below u in DFS tree, then u-v is a bridge''' if low[v] > disc[u]: print ("%d %d" %(u,v)) elif v != parent[u]: # Update low value of u for parent function calls. low[u] = min(low[u], disc[v]) # DFS based function to find all bridges. It uses recursive # function bridgeUtil() def bridge(self): # Mark all the vertices as not visited and Initialize parent and visited, # and ap(articulation point) arrays visited = [False] * (self.V) disc = [float("Inf")] * (self.V) low = [float("Inf")] * (self.V) parent = [-1] * (self.V) # Call the recursive helper function to find bridges # in DFS tree rooted with vertex 'i' for i in range(self.V): if visited[i] == False: self.bridgeUtil(i, visited, parent, low, disc) # Create a graph given in the above diagram g1 = Graph(5) g1.addEdge(1, 0) g1.addEdge(0, 2) g1.addEdge(2, 1) g1.addEdge(0, 3) g1.addEdge(3, 4) print "Bridges in first graph " g1.bridge() g2 = Graph(4) g2.addEdge(0, 1) g2.addEdge(1, 2) g2.addEdge(2, 3) print "\nBridges in second graph " g2.bridge() g3 = Graph (7) g3.addEdge(0, 1) g3.addEdge(1, 2) g3.addEdge(2, 0) g3.addEdge(1, 3) g3.addEdge(1, 4) g3.addEdge(1, 6) g3.addEdge(3, 5) g3.addEdge(4, 5) print "\nBridges in third graph " g3.bridge() #This code is contributed by Neelam Yadav

Output:

Bridges in first graph 3 4 0 3 Bridges in second graph 2 3 1 2 0 1 Bridges in third graph 1 6

**Time Complexity:** The above function is simple DFS with additional arrays. So time complexity is same as DFS which is O(V+E) for adjacency list representation of graph.

**References:**

https://www.cs.washington.edu/education/courses/421/04su/slides/artic.pdf

http://www.slideshare.net/TraianRebedea/algorithm-design-and-complexity-course-8

http://faculty.simpson.edu/lydia.sinapova/www/cmsc250/LN250_Weiss/L25-Connectivity.htm

http://www.youtube.com/watch?v=bmyyxNyZKzI

Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above.