학술논문
Guest Editorial: Deep Neural Networks for Graphs: Theory, Models, Algorithms, and Applications
Document Type
Periodical
Author
Source
IEEE Transactions on Neural Networks and Learning Systems IEEE Trans. Neural Netw. Learning Syst. Neural Networks and Learning Systems, IEEE Transactions on. 35(4):4367-4372 Apr, 2024
Subject
Language
ISSN
2162-237X
2162-2388
2162-2388
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.