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An Experimental Study of the Treewidth of Real-World Graph Data

Authors Silviu Maniu, Pierre Senellart, Suraj Jog

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Author Details

Silviu Maniu
  • LRI, CNRS, Université Paris-Sud, Université Paris-Saclay, Orsay, France
Pierre Senellart
  • DI ENS, ENS, CNRS, PSL University, Paris, France
  • Inria Paris, France
  • LTCI, Télécom ParisTech, Paris, France
Suraj Jog
  • University of Illinois at Urbana–Champaign, Urbana-Champaign, USA

Acknowledgements

We are grateful to Antoine Amarilli and Mikaël Monet for feedback on parts of this paper.

Cite AsGet BibTex

Silviu Maniu, Pierre Senellart, and Suraj Jog. An Experimental Study of the Treewidth of Real-World Graph Data. In 22nd International Conference on Database Theory (ICDT 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 127, pp. 12:1-12:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
https://doi.org/10.4230/LIPIcs.ICDT.2019.12

Abstract

Treewidth is a parameter that measures how tree-like a relational instance is, and whether it can reasonably be decomposed into a tree. Many computation tasks are known to be tractable on databases of small treewidth, but computing the treewidth of a given instance is intractable. This article is the first large-scale experimental study of treewidth and tree decompositions of real-world database instances (25 datasets from 8 different domains, with sizes ranging from a few thousand to a few million vertices). The goal is to determine which data, if any, can benefit of the wealth of algorithms for databases of small treewidth. For each dataset, we obtain upper and lower bound estimations of their treewidth, and study the properties of their tree decompositions. We show in particular that, even when treewidth is high, using partial tree decompositions can result in data structures that can assist algorithms.

Subject Classification

ACM Subject Classification
  • Theory of computation → Logic and databases
Keywords
  • Treewidth
  • Graph decompositions
  • Experiments
  • Query processing

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