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001-es BibID:	BIBFORM124991
035-os BibID:	(Scopus)85202914049 (WOS)001303226000002
Első szerző:	Tiba Attila (informatikus, matematikus)
Cím:	Finding Efficient Graph Embeddings and Processing them by a CNN-based Tool / Tiba, Attila; Hajdu, Andras; Giraszi, Tamas
Dátum:	2024
ISSN:	1573-773X
Megjegyzések:	We introduce new tools to support finding efficient graph embedding techniques for graph databases and to process their outputs using deep learning for classification scenarios. Accordingly, we investigate the possibility of creating an ensemble of different graph embedding methods to raise accuracy and present an interconnected neural network-based ensemble to increase the efficiency of the member classification algorithms. We also introduce a new convolutional neural network-based architecture that can be generally proposed to process vectorized graph data provided by various graph embedding methods and compare it with other architectures in the literature to show the competitiveness of our approach. We also exhibit a statistical-based inhomogeneity level estimation procedure to select the optimal embedding for a given graph database efficiently. The efficiency of our framework is exhaustively tested using several publicly available graph datasets and numerous state-of-the-art graph embedding techniques. Our experimental results for classification tasks have proved the competitiveness of our approach by outperforming the state-of-the-art frameworks. ? The Author(s) 2024.
Tárgyszavak:	Természettudományok Matematika- és számítástudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Convolutional neural networks Ensemble of embeddings Graph classification Graph datasets Graph embedding
Megjelenés:	Neural Processing Letters. - 56 : 5 (2024), p. 1-25. -
További szerzők:	Hajdu András (1973-) (matematikus, informatikus) Girászi Tamás
Pályázati támogatás:	OTKA K143540 Egyéb TKP2021-NKTA-34 Egyéb
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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