Guest Editorial: Deep Neural Networks for Graphs: Theory, Models, Algorithms, and Applications
Zhejiang Normal University · University of Pisa · +5 more institutions
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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.
Citation impact
174
total citations
- FWCI
- 55.10
- Percentile
- 100%
- References
- 61
Citations per year
Authors
7Topics & keywords
Keywords
- Computer science
- Artificial neural network
- Artificial intelligence
- Deep neural networks
- Algorithm
- Cognitive science
- Theoretical computer science
- Psychology
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