Szczegóły publikacji
Opis bibliograficzny
Applying evolutionary techniques to enhance graph convolutional networks for node classification: case studies / Maciej KRZYWDA, Szymon ŁUKASIK, Amir H. Gandomi // W: FedCSIS [Dokument elektroniczny] : proceedings of the 20th conference on Computer Science and Intelligence Systems : September 14–17, 2025, Kraków, Poland / eds. Marek Bolanowski, [et al.]. — Wersja do Windows. — Dane tekstowe. — Warsaw : Polskie Towarzystwo Informatyczne ; [Piscataway] : IEEE, cop. 2025. — ( Annals of Computer Science and Information Systems ; ISSN 2300-5963 ; vol. 43 ). — Dod. ISBN: 979-8-3315-1531-7. — ISBN: 978-83-973291-7-1; e-ISBN: 978-83-973291-6-4. — S. 321–326. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: https://annals-csis.org/Volume_43/pliks/volume_43.pdf [2025-11-04]. — Bibliogr. s. 326, Abstr.
Autorzy (3)
- AGHKrzywda Maciej
- AGHŁukasik Szymon
- Gandomi Amir H.
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 163984 |
|---|---|
| Data dodania do BaDAP | 2025-12-01 |
| DOI | 10.15439/2025F0041 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Creative Commons | |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Konferencja | Conference on Computer Science and Intelligence Systems 2025 |
| Czasopismo/seria | Annals of Computer Science and Information Systems |
Abstract
In recent years, significant efforts have been made to address graph node classification tasks by applying graph neural networks and methods based on label propagation. Despite the progress achieved by these approaches, their success often hinges on complex architectures and algorithms, sometimes leading to the oversight of crucial technical details. In designing artificial neural networks, one crucial aspect of the innovative approach is suggesting a novel neural architecture. Currently used architectures have mostly been developed manually by human experts, which is a time-consuming and error-prone process. That is why the adoption of more sophisticated semi-automatic methods, such as Neural Architecture Search, has become commonplace. This paper introduces and assesses an evolutionary-based approach for the design of graph convolutional neural networks in the context of node classification. Our approach aims to systemati- cally define the graph convolutional networks parameter space, drawing inspiration from recent research on design principles. By doing so, our method seeks to strike a balance between achieving satisfactory performance and optimizing memory and computation resources, thus offering a more efficient alternative to conventional approaches from the neural architecture search area.
