Topological Sort : DFS, BFS and DAG

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Today we will talk about Topological Sorting of a Directed Acyclic Graph (DAG). But before that let us first refresh our memory about some of the important characteristics of Depth First Search (DFS) and Breadth First Search (BFS) :

  • DFS and BFS are two graph search techniques.
  • Both DFS and BFS find all nodes findable, and nothing more. 
  • Both DFS and BFS search all findable nodes in linear time, i.e, O(E + V), where E = number of edges, V = number of vertices.
    Time Complexity of DFS / BFS to search all vertices = O(E + V)
    Reason: O(1) for all nodes, O(1) for all edges,
            because in both the cases, DFS and BFS, 
            we are going to traverse each edge only once 
            and also each vertex only once 
            since you don’t visit an already visited node.

    A DFS will only store as much memory on the stack as is required for the longest root to leaf path in the tree. In other words, it space usage is  O(h) where is the height of the tree. A BFS on the other hand will queue every node at a fixed depth before visiting the next depth. If, for example, you have a complete binary tree, the number of leaves will be  n/= O(nwhere is the number of nodes in the tree. Each of those  n/2 nodes will be queued up at some point.Many trees you will come across are moderately balanced. Even randomly built binary search trees have expected logarithmic height.
    In these cases  O(log n) < O(n) and DFS will therefore use less memory. So DFS is more memory efficient than BFS.

    Depth first search is more memory efficient than breadth first search also because you can backtrack sooner. It is also easier to implement if you use the call stack but this relies on the longest path not overflowing the stack.

    It’s also worth mentioning that BFS is often very easy to understand, by DFS is, in general, more efficient most of the time. But it totally depends on the kind of problem we are trying to solve.

Topological Sort is the most important operation on directed acyclic graphs or DAGs. It orders the vertices on a line such that all directed edges go from left to right. Such an ordering cannot exist if the graph is not a DAG and contains one or more directed cycle(s), because there is no way we can keep going on the right on a line and still return to a vertex already visited (we are talking about Back Edges here).

 If any of you thinking why I did not mention cross-edges along with back-edges, then reason for that is, BFS is used only in Undirected Graphs to determine whether there is a cycle or loop in it by examining if there is a cross-edge. Showing that there is a cross-edge while doing a BFS on Directed graph does not prove that the Directed Graph has a cycle.

If your graph is directed then you have to not just remember if you have visited a node or not, but also how you got there. Otherwise you might think you have found a cycle but in reality all you have is two separate paths A->B but that doesn’t mean there is a path B->A. For example,

BFS Giving Wrong Result in Cycle Detection in a Directed Graph

If you do BFS starting from 0, it will detect as cycle is present but actually there is no cycle.
With a depth first search you can mark nodes as visited as you descend and unmark them as you backtrack.

Every DAG has at least one Topological Ordering.

Motivation for the Topological Sort: Topological Sort is useful in scheduling tasks where precedence ordering matters, i.e, one task needs to be done before starting another. Topological sort can sequence tasks while respecting all sequence constraints without any conflict.

A real world scenario: In most academic programs there are prerequisite courses for taking a specific course. The prerequisite course(s) needs to be completed before taking the course. Topological sorting can give you the sequence in which different courses can be taken by students so that they can complete the pre-requisite courses before taking a course.

When Does A Directed Graph Have A Topological Ordering ?
There is a very clear necessary condition for a directed graph to have a topological sorting, and that is it better be acyclic. So the graph needs to be a DAG (Directed Acyclic Graph). Put in a different way, one of the ways to prove that a graph is a DAG is to show that it has a topological ordering.

Graph G has a directed cycle => G has no Topological Ordering

It is because if a graph G has a directed cycle that means it would have a back-edge, which means when trying to form a topological ordering from at least one of the edges we will have a edge back to one of the earlier edges, or even to the starting edge, in some cases, depending on the graph.It has been seen that having a directed cycle is the only obstruction for having a topological sorting. If you don’t have a directed cycle in a graph G then then you are guaranteed to have a topological ordering for the graph G.

 It has been seen that having a directed cycle is the only obstruction for having a topological sort. If you don’t have a directed cycle in a graph G then then you are guaranteed to have a topological ordering for the graph G.

DFS is a slick and highly efficient way to compute topological sort of a graph in linear time: O(E + V).

Sink Vertex: While trying to implement topological sort using DFS it is important to know what sink vetices are. Sink vertex is the vertex which has no outbound edges. A DAG has to have at least one sink vertex.  If we don’t have a sink vertex then that means that every edge will have at least one outgoing vertex and since we have finite vertices in our graph then by pigeon hole principle that means that, we will be visiting one of the already visited vertices, which implies we would have a directed  cycle. In fact you must convince yourself that one of the sink vertices must be the end vertex of the topological sort. Sink vertices are the only candidates for the final/last vertex position in the topological ordering.

Corollary: A directed graph with no sink vertex must have cycle.

  • DFS Implementation  Mechanism for Topological Sort:

While doing a DFS we will search for the sink vertices, and as we get a sink vertex we will disconnect that vertex from the graph and will then backtrack and search for the next sink vertex along the search path, until all the vertices in the graph are visited. This way we will get all the vertices in totally reverse order of that of what a topological ordering should be. So we would need to reverse the order to get the topological sort.

Why can’t the DFS result be the same as the topological sort and why would we need to find the sink vertices while doing a DFS to have the topological ordering ?

This is because in DFS we print the nodes as we see them, which means when we print a node, it has just been discovered but not yet processed, which means it is in the Visiting state. So DFS gives the order in which the nodes enter the Visiting state and not the Visited state. For topological sorting we need to have the order in which the nodes are completely processed, i.e, the order in which the nodes are marked as Visited. Because when a node is marked Visited then all of its child node have already been processed, so they would be towards the right of the child nodes in the topological sort, as it should be (remember that we are going to reverse the order in which the nodes are marked visited, so that the child nodes go towards the right).


public class Vertex {
    public int val;
    public boolean visiting;
    public boolean visited;
    public List childNodes;

// returns null if no topological sort is possible
public List topological_sort_by_dfs(List<Vertex> graph) {
    Stack stack = new Stack();
    List<Vertex> result = new ArrayList<>();
    for (Vertex vertex : graph) {
        if (!vertex.visited) {
            boolean dfs_result = dfs(vertex, stack);
            // if cycle found then there is no topological sort possible
            if (!dfs_result) {
                return null;
    for (Vertex vertex : stack) {
    return result

private boolean dfs(Vertex vertex, Stack<Vertex> stack) {
    vertex.visiting = true;
    for (Vertex childNode : vertex.childNodes) {
        if (!childNode.visited) {
            // check for back-edge, i.e., cycle
            if (childNode.visiting) {
                return false;
            boolean childResult = dfs(childNode, stack);
            if (!childResult) {
                return false;
    // now that you have completely visited all the
    // childNodes of the vertex, push the vertex in the stack

    // this vertex is processed (all its descendents, i.e,
    // the nodes that are dependent on
    // this vertex along with this vertex itself have been visited 
    // and processed). So mark this vertex as Visited.
    vertex.visited = true;
    // mark vertex as visiting as false since we 
    // have completed visiting all the subtrees of 
    // the vertex, including itself. So the vertex is no more
    // in visiting state because it is done visited.
    // Another reason is (the critical one) now that all the 
    // descendents of the vertex are visited, any future 
    // inbound edge to the vertex won't be a back edge anymore. 
    vertex.visiting = false;  

    return true;
  • Topological Sort by BFS:

Topological Sort can also be implemented by Breadth First Search  as well.
In DFS implementation of Topological Sort we focused on sink vertices, i.e, vertices with zero out-going edges, and then at last had to reverse the order in which we got the sink vertices (which we did by using a stack, which is a Last In First Out data structure). In BFS implementation of the Topological sort we do the opposite: We look for for edges with no inbound edges. And consequently in BFS implementation we don’t have to reverse the order in which we get the vertices, since we get the vertices in order of the topological ordering. We use First-In-First-Out data structure Queue in this implementation. We just search for the vertices with zero inbound edges and put them in the queue till we have processed all the vertices of the graph. Polling vertices from the queue one by one give the topological sort of the graph.

The algorithm goes as follows:

// The input graph is represented by a list of edges, 
// not adjacency matrices.
// the edge goes from edge[0] towards edge[1]
// n_vertices is the total number of vertices present in the graph,
// marked from 1 to n_vertices
// The function below return an ordered list of the vertices of the 
// given graph
// in topological ordering. Since it is an ordered list it 
// will return the
// topological sorted vertices in correct order.
// returns null if no topological sort is possible
public List topological_sort_bfs(int[][] graph, int n_vertices) {
   int[] indegree = new int[n_vertices + 1]; // we are not using the index 0
   // compute the indegree of all the vertices in the graph
   for (int[] edge : graph) {
    Queue currentVerticesWithNoInboundEdge = new LinkedList<>();
    // initialize the queue with all the vertices with no inbound edges
    for (int index = 1; index < n_vertices; index++) {
        if (indegree[index] == 0) {
    List<Integer> result = new ArrayList<>();
    while (!queue.isEmpty()) {
        int vertex1 = queue.poll();
        // now disconnect vertex1 from the graph
        // and decrease the indegree of the other end of the edge by 1
        for (int[] edge : graph) {
            if (edge[0] == vertex1) {
                if (indegree[edge[1]] == 0) {
    // check if the graph had a cycle / loop
    // if the resultset does not contain all the vertices that means
    // there is at least one cycle in the graph
    // that's why the indegree of those vertices participating in the
    // loop could not be made 0 by decrementing.
    if (result.size() != n_vertices) {
    return null;
    return result;

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