Finite Automata for Efficient Graph Recognition

Engelfriet and Vereijken have shown that linear graph grammars based on hyperedge replacement generate graph languages that can be considered as interpretations of regular string languages over typed symbols. In this paper we show that finite automata can be lifted from strings to graphs within the same framework. For the efficient recognition of graphs with these automata, we make them deterministic by a modified powerset construction, and state sufficient conditions under which deterministic finite graph automata recognize graphs without the need to use backtracking.

Authors

Publication Details

Journal
Electronic Proceedings in Theoretical Computer Science
Published
2025-03-25
DOI
https://doi.org/10.4204/eptcs.417.8
Primary Topic
semigroups and automata theory
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Finite Automata for Efficient Graph Recognition

Frank Drewes, Mark Minas, Berthold Hoffmann
Electronic Proceedings in Theoretical Computer Science
semigroups and automata theory
article

Finite Automata for Efficient Graph Recognition

Frank Drewes, Mark Minas, Berthold Hoffmann
article en

Abstract

Engelfriet and Vereijken have shown that linear graph grammars based on hyperedge replacement generate graph languages that can be considered as interpretations of regular string languages over typed symbols. In this paper we show that finite automata can be lifted from strings to graphs within the same framework. For the efficient recognition of graphs with these automata, we make them deterministic by a modified powerset construction, and state sufficient conditions under which deterministic finite graph automata recognize graphs without the need to use backtracking.

Electronic Proceedings in Theoretical Computer ScienceVol. 417
Openalex Percentile: Top 93%
semigroups and automata theory
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.