Shortest Beer Path Queries Based on Graph Decomposition

Authors Tesshu Hanaka , Hirotaka Ono , Kunihiko Sadakane , Kosuke Sugiyama



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Author Details

Tesshu Hanaka
  • Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan
Hirotaka Ono
  • Graduate School of Informatics, Nagoya University, Japan
Kunihiko Sadakane
  • Graduate School of Information Science and Technology, The University of Tokyo, Japan
Kosuke Sugiyama
  • Graduate School of Informatics, Nagoya University, Japan

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Tesshu Hanaka, Hirotaka Ono, Kunihiko Sadakane, and Kosuke Sugiyama. Shortest Beer Path Queries Based on Graph Decomposition. In 34th International Symposium on Algorithms and Computation (ISAAC 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 283, pp. 37:1-37:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.ISAAC.2023.37

Abstract

Given a directed edge-weighted graph G = (V, E) with beer vertices B ⊆ V, a beer path between two vertices u and v is a path between u and v that visits at least one beer vertex in B, and the beer distance between two vertices is the shortest length of beer paths. We consider indexing problems on beer paths, that is, a graph is given a priori, and we construct some data structures (called indexes) for the graph. Then later, we are given two vertices, and we find the beer distance or beer path between them using the data structure. For such a scheme, efficient algorithms using indexes for the beer distance and beer path queries have been proposed for outerplanar graphs and interval graphs. For example, Bacic et al. (2021) present indexes with size O(n) for outerplanar graphs and an algorithm using them that answers the beer distance between given two vertices in O(α(n)) time, where α(⋅) is the inverse Ackermann function; the performance is shown to be optimal. This paper proposes indexing data structures and algorithms for beer path queries on general graphs based on two types of graph decomposition: the tree decomposition and the triconnected component decomposition. We propose indexes with size O(m+nr²) based on the triconnected component decomposition, where r is the size of the largest triconnected component. For a given query u,v ∈ V, our algorithm using the indexes can output the beer distance in query time O(α(m)). In particular, our indexing data structures and algorithms achieve the optimal performance (the space and the query time) for series-parallel graphs, which is a wider class of outerplanar graphs.

Subject Classification

ACM Subject Classification
  • Theory of computation → Data structures design and analysis
Keywords
  • graph algorithm
  • shortest path problem
  • SPQR tree

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References

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