Efficient methods for the distance-based critical node detection problem in complex networks
Alozie, Glory Uche and Arulselvan, Ashwin and Akartunali, Kerem and Pasiliao Jr, Eduardo l. (2021) Efficient methods for the distance-based critical node detection problem in complex networks. Computers & Operations Research, 131. 105254. ISSN 0305-0548 (https://doi.org/10.1016/j.cor.2021.105254)
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Abstract
An important problem in network survivability assessment is the identification of critical nodes. The distance-based critical node detection problem addresses the issues of internal cohesiveness and actual distance connectivity overlooked by the traditional critical node detection problem. In this study, we consider the distance-based critical node detection problem which seeks to optimise some distance-based connectivity metric subject to budgetary constraints on the critical node set. We exploit the structure of the problem to derive new path-based integer linear programming formulations that are scalable when compared to an existing compact model. We develop an efficient algorithm for the separation problem that is based on breadth first search tree generation. We also study some valid inequalities to strengthen the formulations and a heuristic to improve primal bounds. We have applied our models and algorithm to two different classes of the problems determined by the distance based connectivity functions. Extensive computational experiments on both real-world and randomly generated network instances, show that the proposed approach is computationally more efficient than the existing compact model especially for larger instances where connections between nodes consist of a small number of hops. Our computational experiments on both classes of distance-based critical node detection problem provide good numerical evidence to support the importance of defining appropriate metrics for specific network applications.
ORCID iDs
Alozie, Glory Uche ORCID: https://orcid.org/0000-0001-8750-6394, Arulselvan, Ashwin ORCID: https://orcid.org/0000-0001-9772-5523, Akartunali, Kerem ORCID: https://orcid.org/0000-0003-0169-3833 and Pasiliao Jr, Eduardo l.;-
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Item type: Article ID code: 75605 Dates: DateEvent31 July 2021Published24 February 2021Published Online15 February 2021AcceptedSubjects: Science > Mathematics > Electronic computers. Computer science Department: Strathclyde Business School > Management Science
Strategic Research Themes > Ocean, Air and SpaceDepositing user: Pure Administrator Date deposited: 02 Mar 2021 16:25 Last modified: 25 Dec 2024 01:22 URI: https://strathprints.strath.ac.uk/id/eprint/75605