Guest Editorial: Deep Neural Networks for Graphs: Theory, Models, Algorithms, and Applications
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Micheli, A
Wang, YG
Pan, S
Lio, P
Gnecco, GS
Sanguineti, M
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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.
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IEEE Transactions on Neural Networks and Learning Systems
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35
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4
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Neural networks
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Li, M; Micheli, A; Wang, YG; Pan, S; Lio, P; Gnecco, GS; Sanguineti, M, Guest Editorial: Deep Neural Networks for Graphs: Theory, Models, Algorithms, and Applications, IEEE Transactions on Neural Networks and Learning Systems, 2024, 35 (4), pp. 4367-4372