Axiomatization of centrality measures often involves proving that something cannot hold by providing a counterexample (i.e., a graph for which that specific centrality index fails to have a given property). In the context of geometric centralities, building such counterexamples requires constructing a graph with specific distance counts between nodes, as expressed by its distance-count matrix. We prove that deciding whether a matrix is the distance-count matrix of a graph is strongly NP-complete. This negative result implies that a brute-force approach to building this kind of counterexample is out of question, and cleverer approaches are required.

Recognizing Distance-Count Matrices is Difficult / P. Boldi, F. Furia, C. Prezioso, I. Stewart. - (2025 Aug 26). [10.48550/arXiv.2508.18857]

Recognizing Distance-Count Matrices is Difficult

P. Boldi
Primo
;
F. Furia
Secondo
;
2025

Abstract

Axiomatization of centrality measures often involves proving that something cannot hold by providing a counterexample (i.e., a graph for which that specific centrality index fails to have a given property). In the context of geometric centralities, building such counterexamples requires constructing a graph with specific distance counts between nodes, as expressed by its distance-count matrix. We prove that deciding whether a matrix is the distance-count matrix of a graph is strongly NP-complete. This negative result implies that a brute-force approach to building this kind of counterexample is out of question, and cleverer approaches are required.
geometric centralities; centrality axioms; NP-completeness
Settore INFO-01/A - Informatica
26-ago-2025
https://arxiv.org/abs/2508.18857
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1182575
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