How to Attack the NP-complete Dag Realization Problem in Practice

TL;DR

This paper investigates the NP-complete problem of realizing directed acyclic graphs from degree sequences, introducing efficient algorithms, heuristics, and structural insights that perform well on both synthetic and real-world instances.

Contribution

It presents new exponential and linear-time algorithms, structural properties, and a randomized heuristic for the NP-complete DAG realization problem, with strong empirical performance.

Findings

The linear-time heuristic solves most instances efficiently.

Structural characteristics predict realization difficulty.

The algorithms perform well on real-world data.

Abstract

We study the following fundamental realization problem of directed acyclic graphs (dags). Given a sequence S:=(a_1,b_1),...,(a_n, b_n) with a_i, b_i in Z_0^+, does there exist a dag (no parallel arcs allowed) with labeled vertex set V:= {v_1,...,v_n} such that for all v_i in V indegree and outdegree of v_i match exactly the given numbers a_i and b_i, respectively? Recently this decision problem has been shown to be NP-complete by Nichterlein (2011). However, we can show that several important classes of sequences are efficiently solvable. In previous work (Berger and Mueller-Hannemann, FCT2011), we have proved that yes-instances always have a special kind of topological order which allows us to reduce the number of possible topological orderings in most cases drastically. This leads to an exact exponential-time algorithm which significantly improves upon a straightforward approach. Moreover, a combination of this exponential-time algorithm with a special strategy gives a linear-time algorithm. Interestingly, in systematic experiments we observed that we could solve a huge majority of all instances by the linear-time heuristic. This motivates us to develop characteristics like dag density and "distance to provably easy sequences" which can give us an indicator how easy or difficult a given sequence can be realized. Furthermore, we propose a randomized algorithm which exploits our structural insight on topological sortings and uses a number of reduction rules. We observe that it clearly outperforms all other variants and behaves surprisingly well for almost all instances. Another striking observation is that our simple linear-time algorithm solves a set of real-world instances from different domains, namely ordered binary decision diagrams (OBDDs), train and flight schedules, as well as instances derived from food-web networks without any exception.