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Main Authors: Dondi, Riccardo, Hosseinzadeh, Mohammad Mehdi
Format: Preprint
Published: 2026
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Online Access:https://arxiv.org/abs/2604.25589
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author Dondi, Riccardo
Hosseinzadeh, Mohammad Mehdi
author_facet Dondi, Riccardo
Hosseinzadeh, Mohammad Mehdi
contents This paper addresses the problem of identifying time interval separators in temporal networks. We introduce d-MinIntSep, a new variant of the temporal separator problem, which models failures as time intervals assigned to vertices and aims to block all temporal paths between a source and a target that can be completed within a given deadline d. We prove that the d-MinIntSep problem is NP-hard and hard to approximate within a logarithmic function of the size of the vertex set, assuming P is not equal to NP, and we propose an Integer Linear Programming (ILP) formulation to compute minimum interval separators. This latter method is evaluated on synthetic and real-world temporal networks derived from transportation datasets. The experimental results show that the running time is strongly influenced by the temporal dimension, the imposed deadline, and the density of temporal paths.
format Preprint
id arxiv_https___arxiv_org_abs_2604_25589
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Testing Robustness of Temporal Transportation Networks via Interval Separators
Dondi, Riccardo
Hosseinzadeh, Mohammad Mehdi
Data Structures and Algorithms
This paper addresses the problem of identifying time interval separators in temporal networks. We introduce d-MinIntSep, a new variant of the temporal separator problem, which models failures as time intervals assigned to vertices and aims to block all temporal paths between a source and a target that can be completed within a given deadline d. We prove that the d-MinIntSep problem is NP-hard and hard to approximate within a logarithmic function of the size of the vertex set, assuming P is not equal to NP, and we propose an Integer Linear Programming (ILP) formulation to compute minimum interval separators. This latter method is evaluated on synthetic and real-world temporal networks derived from transportation datasets. The experimental results show that the running time is strongly influenced by the temporal dimension, the imposed deadline, and the density of temporal paths.
title Testing Robustness of Temporal Transportation Networks via Interval Separators
topic Data Structures and Algorithms
url https://arxiv.org/abs/2604.25589