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Johnson’s algorithm for All-pairs shortest paths

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  • Difficulty Level : Hard
  • Last Updated : 22 Mar, 2023
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The problem is to find the shortest paths between every pair of vertices in a given weighted directed Graph and weights may be negative. We have discussed Floyd Warshall Algorithm for this problem.  The time complexity of the Floyd Warshall Algorithm is Θ(V3). 

Using Johnson’s algorithm, we can find all pair shortest paths in O(V2log V + VE) time. Johnson’s algorithm uses both Dijkstra and Bellman-Ford as subroutines. If we apply Dijkstra’s Single Source shortest path algorithm for every vertex, considering every vertex as the source, we can find all pair shortest paths in O(V*VLogV) time. 

So using Dijkstra’s single-source shortest path seems to be a better option than Floyd Warshall’s Algorithm( , but the problem with Dijkstra’s algorithm is, that it doesn’t work for negative weight edge. The idea of Johnson’s algorithm is to re-weight all edges and make them all positive, then apply Dijkstra’s algorithm for every vertex. 

How to transform a given graph into a graph with all non-negative weight edges? 

One may think of a simple approach of finding the minimum weight edge and adding this weight to all edges. Unfortunately, this doesn’t work as there may be a different number of edges in different paths (See this for an example). If there are multiple paths from a vertex u to v, then all paths must be increased by the same amount, so that the shortest path remains the shortest in the transformed graph. The idea of Johnson’s algorithm is to assign a weight to every vertex. Let the weight assigned to vertex u be h[u]. 

We reweight edges using vertex weights. For example, for an edge (u, v) of weight w(u, v), the new weight becomes w(u, v) + h[u] – h[v]. The great thing about this reweighting is, that all set of paths between any two vertices is increased by the same amount and all negative weights become non-negative. Consider any path between two vertices s and t, the weight of every path is increased by h[s] – h[t], and all h[] values of vertices on the path from s to t cancel each other. 

How do we calculate h[] values? 

Bellman-Ford algorithm is used for this purpose. Following is the complete algorithm. A new vertex is added to the graph and connected to all existing vertices. The shortest distance values from the new vertex to all existing vertices are h[] values. 


  1. Let the given graph be G. Add a new vertex s to the graph, add edges from the new vertex to all vertices of G. Let the modified graph be G’. 
  2. Run the Bellman-Ford algorithm on G’ with s as the source. Let the distances calculated by Bellman-Ford be h[0], h[1], .. h[V-1]. If we find a negative weight cycle, then return. Note that the negative weight cycle cannot be created by new vertex s as there is no edge to s. All edges are from s. 
  3. Reweight the edges of the original graph. For each edge (u, v), assign the new weight as “original weight + h[u] – h[v]”. 
  4. Remove the added vertex s and run Dijkstra’s algorithm for every vertex. 

How does the transformation ensure nonnegative weight edges? 

The following property is always true about h[] values as they are the shortest distances.

   h[v] <= h[u] + w(u, v) 

The property simply means that the shortest distance from s to v must be smaller than or equal to the shortest distance from s to u plus the weight of the edge (u, v). The new weights are w(u, v) + h[u] – h[v]. The value of the new weights must be greater than or equal to zero because of the inequality “h[v] <= h[u] + w(u, v)”. 

Example: Let us consider the following graph. 


 We add a source s and add edges from s to all vertices of the original graph. In the following diagram s is 4. 



We calculate the shortest distances from 4 to all other vertices using Bellman-Ford algorithm. The shortest distances from 4 to 0, 1, 2 and 3 are 0, -5, -1 and 0 respectively, i.e., h[] = {0, -5, -1, 0}. Once we get these distances, we remove the source vertex 4 and reweight the edges using following formula. w(u, v) = w(u, v) + h[u] – h[v].


Since all weights are positive now, we can run Dijkstra’s shortest path algorithm for every vertex as the source. 


#define INF 99999
using namespace std;
// Number of vertices in the graph
#define V 4
// A utility function to find the vertex with minimum distance value, from
// the set of vertices not yet included in shortest path tree
int minDistance(int dist[], bool sptSet[])
   // Initialize min value
   int min = INT_MAX, min_index;
   for (int v = 0; v < V; v++)
     if (sptSet[v] == false && dist[v] <= min)
         min = dist[v], min_index = v;
   return min_index;
// A utility function to print the constructed distance array
int printSolution(int dist[][V])
   printf ("Following matrix shows the shortest distances"
           " between every pair of vertices \n");
   for (int i = 0; i < V; i++)
       for (int j = 0; j < V; j++)
           if (dist[i][j] == INF)
               printf("%7s", "INF");
               printf ("%7d", dist[i][j]);
// Solves the all-pairs shortest path problem using Johnson's algorithm
void floydWarshall (int graph[][V])
    int dist[V][V], i, j, k;
    /* Initialize the solution matrix same as input graph matrix. Or
       we can say the initial values of shortest distances are based
       on shortest paths considering no intermediate vertex. */
    for (i = 0; i < V; i++)
        for (j = 0; j < V; j++)
            dist[i][j] = graph[i][j];
    /* Add all vertices one by one to the set of intermediate vertices.
      ---> Before start of a iteration, we have shortest distances between all
      pairs of vertices such that the shortest distances consider only the
      vertices in set {0, 1, 2, .. k-1} as intermediate vertices.
      ----> After the end of a iteration, vertex no. k is added to the set of
      intermediate vertices and the set becomes {0, 1, 2, .. k} */
    for (k = 0; k < V; k++)
        // Pick all vertices as source one by one
        for (i = 0; i < V; i++)
            // Pick all vertices as destination for the
            // above picked source
            for (j = 0; j < V; j++)
                // If vertex k is on the shortest path from
                // i to j, then update the value of dist[i][j]
                if (dist[i][k] + dist[k][j] < dist[i][j])
                    dist[i][j] = dist[i][k] + dist[k][j];
    // Print the shortest distance matrix
// driver program to test above function
int main()
   /* Let us create the following weighted graph
        |         /|\
      5 |          |
        |          | 1
       \|/         |
            3           */
   int graph[V][V] = { {0,   5,  INF, 10},
                        {INF, 0,   3, INF},
                        {INF, INF, 0,   1},
                        {INF, INF, INF, 0}
    // Print the solution
    return 0;


Following matrix shows the shortest distances between every pair of vertices 
      0      5      8      9
    INF      0      3      4
    INF    INF      0      1
    INF    INF    INF      0

Time Complexity: The main steps in the algorithm are Bellman-Ford Algorithm called once and Dijkstra called V times. Time complexity of Bellman Ford is O(VE) and time complexity of Dijkstra is O(VLogV). So overall time complexity is O(V2log V + VE). 

The time complexity of Johnson’s algorithm becomes the same as Floyd Warshall’s Algorithm (

when the graph is complete (For a complete graph E = O(V2). But for sparse graphs, the algorithm performs much better than Floyd Warshall’s Algorithm( ). 

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