Finding a minimum vertex cover in a network is a fundamental NP-complete graph problem. One way to deal with its computational hardness, is to trade the qualitative performance of an algorithm (allowing non-optimal outputs) for an improved running time. For the vertex cover problem, there is a gap between theory and practice when it comes to understanding this trade-off. On the one hand, it is known that it is NP-hard to approximate a minimum vertex cover within a factor of $\sqrt{2}$. On the other hand, a simple greedy algorithm yields close to optimal approximations in practice. A promising approach towards understanding this discrepancy is to recognize the differences between theoretical worst-case instances and real-world networks. Following this direction, we narrow the gap between theory and practice by providing an algorithm that efficiently computes nearly optimal vertex cover approximations on hyperbolic random graphs; a network model that closely resembles real-world networks in terms of degree distribution, clustering, and the small-world property. More precisely, our algorithm computes a (1+𝑜(1))-approximation, asymptotically almost surely, and has a running time of ${\mathcal {O}}(m \log (n))$. ... mehr

Zugehörige Institution(en) am KIT |
Institut für Theoretische Informatik (ITI) |

Publikationstyp |
Zeitschriftenaufsatz |

Publikationsjahr |
2023 |

Sprache |
Englisch |

Identifikator |
ISSN: 0178-4617, 1432-0541 KITopen-ID: 1000160583 |

Erschienen in |
Algorithmica |

Verlag |
Springer |

Vorab online veröffentlicht am |
27.06.2023 |

Nachgewiesen in |
Dimensions Web of Science Scopus |

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