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Journal of Graph Algorithms and Applications
Article . 2025 . Peer-reviewed
License: CC BY
Data sources: Crossref
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https://dx.doi.org/10.48550/ar...
Article . 2023
License: CC BY
Data sources: Datacite
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Scalable Algorithms for 2-Packing Sets on Arbitrary Graphs

Authors: Jannick Borowitz; Ernestine Großmann; Christian Schulz; Dominik Schweisgut;

Scalable Algorithms for 2-Packing Sets on Arbitrary Graphs

Abstract

A 2-packing set for an undirected graph $G=(V,E)$ is defined as a subset $\mathcal{S}\subseteq V$ such that for each pair of vertices $v_1 \neq v_2 \in \mathcal{S}$, the shortest path between $v_1$ and $v_2$ has at least length three. Finding a 2-packing set of maximum cardinality is an NP-hard problem. We develop a new approach to solve this problem on arbitrary graphs using its close relation to the independent set problem. Our approach uses new data reduction rules as well as a graph transformation. Experiments show that this technique outperforms the state-of-the-art for arbitrary graphs with respect to solution quality. Furthermore, we can compute solutions multiple orders of magnitude faster than previously possible. Our approach solves 63% of the graphs in the tested data set to optimality in under a second. In contrast, the competitor for arbitrary graphs can only solve 5% of these graphs to optimality even with a 10-hour time limit. Moreover, our approach can solve a wide range of large instances that have previously been unsolved.

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Keywords

FOS: Computer and information sciences, Computer Science - Data Structures and Algorithms, Data Structures and Algorithms (cs.DS)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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