@inproceedings{b722f1d6a05a4e849d356c41a6d16d27,
title = "Efficient algorithms for measuring the funnel-likeness of DAGs",
abstract = "Funnels are 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 analog to trees for directed graphs that is more restrictive than DAGs but more expressive than 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 study the NP-hard problem of computing the arc-deletion distance to a funnel of a given DAG. We develop efficient exact and approximation algorithms for the problem and test them on synthetic random graphs and real-world graphs.",
author = "Millani, {Marcelo Garlet} and Hendrik Molter and Rolf Niedermeier and Manuel Sorge",
note = "Publisher Copyright: {\textcopyright} 2018, Springer International Publishing AG, part of Springer Nature.; 5th International Symposium on Combinatorial Optimization, ISCO 2018 ; Conference date: 11-04-2018 Through 13-04-2018",
year = "2018",
month = jan,
day = "1",
doi = "10.1007/978-3-319-96151-4_16",
language = "English",
isbn = "9783319961507",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "183--195",
editor = "Giovanni Rinaldi and Mahjoub, {A. Ridha} and Jon Lee",
booktitle = "Combinatorial Optimization - 5th International Symposium, ISCO 2018, Revised Selected Papers",
address = "Germany",
}