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.File | Dimensione | Formato | |
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Ming_Guest-Editorial_2024.pdf
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