Given a Weighted Directed Acyclic Graph (DAG) and a source vertex in it, find the longest distances from source vertex to all other vertices in the given graph.
We have already discussed how we can find Longest Path in Directed Acyclic Graph(DAG) in Set 1. In this post, we will discuss another interesting solution to find longest path of DAG that uses algorithm for finding Shortest Path in a DAG.
The idea is to negate the weights of the path and find the shortest path in the graph. A longest path between two given vertices s and t in a weighted graph G is the same thing as a shortest path in a graph G’ derived from G by changing every weight to its negation. Therefore, if shortest paths can be found in G’, then longest paths can also be found in G.
Below is the step by step process of finding longest paths –
We change weight of every edge of given graph to its negation and initialize distances to all vertices as infinite and distance to source as 0, then we find a topological sorting of the graph which represents a linear ordering of the graph. When we consider a vertex u in topological order, it is guaranteed that we have considered every incoming edge to it. i.e. We have already found shortest path to that vertex and we can use that info to update shorter path of all its adjacent vertices. Once we have topological order, we one by one process all vertices in topological order. For every vertex being processed, we update distances of its adjacent vertex using shortest distance of current vertex from source vertex and its edge weight. i.e.
for every adjacent vertex v of every vertex u in topological order if (dist[v] > dist[u] + weight(u, v)) dist[v] = dist[u] + weight(u, v)
Once we have found all shortest paths from the source vertex, longest paths will be just negation of shortest paths.
Below is its C++ implementation –
Following are longest distances from source vertex 1 INT_MIN 0 2 9 8 10
Time Complexity: Time complexity of topological sorting is O(V + E). After finding topological order, the algorithm process all vertices and for every vertex, it runs a loop for all adjacent vertices. As total adjacent vertices in a graph is O(E), the inner loop runs O(V + E) times. Therefore, overall time complexity of this algorithm is O(V + E).
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- Longest path in a directed Acyclic graph | Dynamic Programming
- Shortest Path in Directed Acyclic Graph
- Clone a Directed Acyclic Graph
- All Topological Sorts of a Directed Acyclic Graph
- Assign directions to edges so that the directed graph remains acyclic
- Number of paths from source to destination in a directed acyclic graph
- Convert the undirected graph into directed graph such that there is no path of length greater than 1
- Find if there is a path between two vertices in a directed graph
- Path with minimum XOR sum of edges in a directed graph
- Shortest path with exactly k edges in a directed and weighted graph
- Shortest path with exactly k edges in a directed and weighted graph | Set 2
- Shortest path in a directed graph by Dijkstra’s algorithm
- Minimum Cost of Simple Path between two nodes in a Directed and Weighted Graph
- DFS for a n-ary tree (acyclic graph) represented as adjacency list
- Count ways to change direction of edges such that graph becomes acyclic
- Calculate number of nodes between two vertices in an acyclic Graph by Disjoint Union method
- Convert undirected connected graph to strongly connected directed graph
- Detect Cycle in a Directed Graph
- Hierholzer's Algorithm for directed graph