Graph neural networks for network analysis
<p>With an increasing number of applications where data can be represented as graphs, graph neural networks (GNNs) are a useful tool to apply deep learning to graph data. Signed and directed networks are important forms of networks that are linked to many real-world problems, such as ranking f...
Main Author: | He, Y |
---|---|
Other Authors: | Dong, X |
Format: | Thesis |
Language: | English |
Published: |
2024
|
Subjects: |
Similar Items
-
JGNN: Graph Neural Networks on native Java
by: Emmanouil Krasanakis, et al.
Published: (2023-07-01) -
Auto-GNN: Neural architecture search of graph neural networks
by: Kaixiong Zhou, et al.
Published: (2022-11-01) -
Improved Lipophilicity and Aqueous Solubility Prediction with Composite Graph Neural Networks
by: Oliver Wieder, et al.
Published: (2021-10-01) -
GNNGLY: Graph Neural Networks for Glycan Classification
by: Alhasan Alkuhlani, et al.
Published: (2023-01-01) -
Investigating Transfer Learning in Graph Neural Networks
by: Nishai Kooverjee, et al.
Published: (2022-04-01)