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. BoldiPrimo
;F. FuriaSecondo
;
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.| File | Dimensione | Formato | |
|---|---|---|---|
|
2508.18857v1.pdf
accesso aperto
Tipologia:
Pre-print (manoscritto inviato all'editore)
Licenza:
Creative commons
Dimensione
375.19 kB
Formato
Adobe PDF
|
375.19 kB | Adobe PDF | Visualizza/Apri |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.




