Deep neural networks for graphs (DNNGs) represent an emerging field that studies how the deep learning method can be generalized to graph-structured data. Since graphs are a powerful and flexible tool to represent complex information in the form of patterns and their relationships, ranging from molecules to protein-to-protein interaction networks, to social or transportation networks, or up to knowledge graphs, potentially modeling systems at very different scales, these methods have been exploited for many application domains.

Guest Editorial: Deep Neural Networks for Graphs: Theory, Models, Algorithms, and Applications / Li, Ming; Micheli, Alessio; Wang, Yu Guang; Pan, Shirui; Lio, Pietro; Gnecco, Giorgio Stefano; Sanguineti, Marcello. - In: IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS. - ISSN 2162-237X. - 35:4(2024), pp. 4367-4372. [10.1109/tnnls.2024.3371592]

Guest Editorial: Deep Neural Networks for Graphs: Theory, Models, Algorithms, and Applications

Lio, Pietro;
2024

Abstract

Deep neural networks for graphs (DNNGs) represent an emerging field that studies how the deep learning method can be generalized to graph-structured data. Since graphs are a powerful and flexible tool to represent complex information in the form of patterns and their relationships, ranging from molecules to protein-to-protein interaction networks, to social or transportation networks, or up to knowledge graphs, potentially modeling systems at very different scales, these methods have been exploited for many application domains.
2024
01 Pubblicazione su rivista::01m Editorial/Introduzione in rivista
Guest Editorial: Deep Neural Networks for Graphs: Theory, Models, Algorithms, and Applications / Li, Ming; Micheli, Alessio; Wang, Yu Guang; Pan, Shirui; Lio, Pietro; Gnecco, Giorgio Stefano; Sanguineti, Marcello. - In: IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS. - ISSN 2162-237X. - 35:4(2024), pp. 4367-4372. [10.1109/tnnls.2024.3371592]
File allegati a questo prodotto
File Dimensione Formato  
Ming_Guest-Editorial_2024.pdf

accesso aperto

Note: https://ieeexplore.ieee.org/ielx7/5962385/10492491/10492652.pdf
Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Creative commons
Dimensione 5.52 MB
Formato Adobe PDF
5.52 MB Adobe PDF

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1728692
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 61
  • ???jsp.display-item.citation.isi??? 39
social impact