The Problem

I have a set of edges (a, b), where a and b are nodes in a directed, acyclic graph (DAG).

The number of edges is guaranteed to be the number of nodes - 1.

I am looking for an algorithm that finds a sequence of nodes [n_1, ... n_n] so that the sequence contains all nodes in the graph, and that all edges (n_i, n_i+1) for 0 < i < n exist in edges. If that sequence is not possible because the given set of edges is not equal to the set of edges required, then I need an error to be thrown.

Edit: the graph may have an arbitrary number of disconnected components. I obviously don't consider a graph with multiple disconnected components as linear.

Ideas so far

Note that I am checking that the number of edges is exactly the number of edges required for such a sequence to exist. As a result, the algorithm can fail when there are two edges that share a source or a destination. However, I still need to get that sequence.

I realize that topological sort returns a linear order of a directed graph, which satisfies my requirements. However, I still want to fail when the graph is not exactly linear like that, instead of getting a linear order anyways.

Maybe I can validate, then do a topological sort. But that sounds inefficient. I am also not sure about the formal names of things in this problem, so it's hard to simply look up an algorithm. I feel like I am missing some simple connection or trick here.

Could you provide me with an algorithm that accomplishes this? I'll take pseudocode or any language of your choice.

  • 1
    $\begingroup$ Have you tried thinking of an algorithm using BFS or DFS? $\endgroup$
    – D.W.
    May 3, 2020 at 15:37

2 Answers 2


Here is a quite non-optimal algorithm, to get you started.

Go over all edges $(a,b)$. For each edge, go again over all edges, and check how many times $a$ appears. Stop once you discover $a$ that appears only once (if this never happens, reject). We will take $n_1 = a$.

Go over all edges again, looking for the unique edge $(n_1,b)$ in which $n_1$ appears. Take $n_2 = b$.

Go over all edges again, looking for an edge $(n_2,c)$ in which $n_2$ appears on the left (if there is no such edge, reject). Take $n_3 = c$.

Continue in this way, finding $n_4,\ldots,n_n$.

This algorithm runs in $O(n^2)$. Using hashing, you should be able to improve it to $O(n)$.

  • $\begingroup$ Are you sure this works? If there are any two edges (a, b) and (c, d) where a = c, then the graph is not linear. Every node should exist exactly once as a the source of an edge and once as a destination. Otherwise it should fail. $\endgroup$
    – Dracam
    May 3, 2020 at 15:46
  • $\begingroup$ You also need to check that the different $n_i$ that you find are distinct. $\endgroup$ May 3, 2020 at 15:47

I figured out an algorithm that at least passes my test cases:

let N = set of all nodes
let E = set of all edges

if (|E| != |N|-1) fail

let possibleStartNodes = new set containing all of N
let nexts = new dictionary from node -> node

foreach (prev, next) in E:
  if (next not in possibleStartNodes) fail 
  remove next from possibleStartNodes
  nexts[prev] = next
end foreach

if (|possibleStartNodes| != 1) fail

let currentNode = the one node in possibleStartNodes
let ordering = new list

while (nexts contains value for currentNode) 
  currentNode = next
end while

return ordering

$O(n)$ looks pretty reasonable to me. Are there any ways to improve?

Edit: I would appreciate any hints about the correct technical terms for the concepts I'm using


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