Recognition Complexity of Subgraphs of $${\textbf {k}}$$-Connected Planar Cubic Graphs

Abstract We study the recognition complexity of subgraphs of k -connected planar cubic graphs where $${k \in \{0, 1, 2, 3\}}$$ . We present polynomial-time algorithms to recognize subgraphs of 1- and 2-connected planar cubic graphs, both in the variable and fixed embedding setting. The main tools involve the Generalized (Anti)factor -problem for the fixed embedding case, and SPQR-trees for the variable embedding case. Secondly, we prove -hardness of recognizing subgraphs of 3-connected planar cubic graphs in the variable embedding setting.

Authors

Institutions

Publication Details

Journal
Algorithmica
Published
2025-11-25
DOI
https://doi.org/10.1007/s00453-025-01350-8
Primary Topic
Advanced Graph Theory Research
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Recognition Complexity of Subgraphs of $${\textbf {k}}$$-Connected Planar Cubic Graphs

Paul Jungeblut, Torsten Ueckerdt, Miriam Goetze
Algorithmica
Advanced Graph Theory Research
article

Recognition Complexity of Subgraphs of $${\textbf {k}}$$-Connected Planar Cubic Graphs

Paul Jungeblut, Torsten Ueckerdt, Miriam Goetze
article en

Abstract

Abstract We study the recognition complexity of subgraphs of k -connected planar cubic graphs where $${k \in \{0, 1, 2, 3\}}$$ . We present polynomial-time algorithms to recognize subgraphs of 1- and 2-connected planar cubic graphs, both in the variable and fixed embedding setting. The main tools involve the Generalized (Anti)factor -problem for the fixed embedding case, and SPQR-trees for the variable embedding case. Secondly, we prove -hardness of recognizing subgraphs of 3-connected planar cubic graphs in the variable embedding setting.

AlgorithmicaVol. 88(1)
Karlsruhe Institute of Technology (DE)
Deutsche Forschungsgemeinschaft
Openalex Percentile: Top 63%
Advanced Graph Theory Research
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.