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.F. (STUDIES IN COMPUTATIONAL INTELLIGENCE). - In: Complex Networks & Their Applications XIV. (Volume 2) / [a cura di] H.e Cherifi, L. M. Rocha, C. Cherifi, M. Zeynep Ertem. - Prima edizione. - [s.l] : Springer, 2026. - ISBN 978-3-032-16648-7. - pp. 267-281 (( 14. COMPLEX NETWORKS 2025 : XIV International Conference on Complex Networks and their Applications : December, 9th to 11th Binghamton (USA) [10.1007/978-3-032-16649-4_23].
Recognizing Distance-Count Matrices Is Difficult
P. Boldi
Primo
;F. FuriaSecondo
;C. PreziosoPenultimo
;
2026
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.| File | Dimensione | Formato | |
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