Szczegóły publikacji
Opis bibliograficzny
Hierarchical structural information – theory and applications / Marzena BIELECKA, Andrzej BIELECKI, Aleksander SUCHORAB, Igor WOJNICKI // W: Computational Science – ICCS 2025 : 25th international conference : Singapore, Singapore, July 7–9, 2025 : proceedings, Pt. 3 / eds. Michael H. Lees [et al.]. — Cham : Springer Nature Switzerland, cop. 2025. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 15905). — ISBN: 978-3-031-97631-5; e-ISBN: 978-3-031-97632-2. — S. 48–59. — Bibliogr., Abstr. — Publikacja dostępna online od: 2025-07-05
Autorzy (4)
Dane bibliometryczne
| ID BaDAP | 161042 |
|---|---|
| Data dodania do BaDAP | 2025-09-16 |
| DOI | 10.1007/978-3-031-97632-2_4 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International Conference on Computational Science 2025 |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
This paper introduces a novel measure to quantify structural information in hierarchical graphs. It addresses the limitation of current methods that do not adequately account for hierarchical structures. By considering inner structural information and distinguishability of higher-level vertices, the proposed measure captures the additional information generated by the hierarchy. The hypothesis that hierarchical graphs contain more structural information is validated using the “Countries” dataset. The results demonstrate a measurable increase in the information content when the hierarchical structure is considered, compared to a simple graph representation. This highlights the importance of recognizing and utilizing hierarchy to enhance the informational richness of graphs, potentially improving the performance of graph-based machine learning models.
