Efficient algorithms for measuring the funnel-likeness of DAGs

Marcelo Garlet Millani (), Hendrik Molter (), Rolf Niedermeier () and Manuel Sorge ()
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Marcelo Garlet Millani: TU Berlin
Hendrik Molter: TU Berlin
Rolf Niedermeier: TU Berlin
Manuel Sorge: TU Berlin

Journal of Combinatorial Optimization, 2020, vol. 39, issue 1, No 14, 216-245

Abstract: Abstract We propose funnels as a new natural subclass of DAGs. Intuitively, a DAG is a funnel if every source-sink path can be uniquely identified by one of its arcs. Funnels are an analogue to trees for directed graphs, being more restrictive than DAGs but more expressive than mere in-/out-trees. Computational problems such as finding vertex-disjoint paths or tracking the origin of memes remain NP-hard on DAGs while on funnels they become solvable in polynomial time. Our main focus is the algorithmic complexity of finding out how funnel-like a given DAG is. To this end, we identify the NP-hard problem of computing the arc-deletion distance of a given DAG to a funnel. We develop efficient exact and approximation algorithms for the problem and test them on synthetic random graphs and real-world graphs.

Keywords: Directed graphs; Acyclic digraph; NP-hard problems; Approximation hardness; Fixed-parameter tractability; Approximation algorithms; Graph parameters; Experiments (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10878-019-00464-4

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