Guest Editorial: Deep Neural Networks for Graphs: Theory, Models, Algorithms, and Applications

Loading...
Thumbnail Image
File version

Accepted Manuscript (AM)

Author(s)
Li, M
Micheli, A
Wang, YG
Pan, S
Lio, P
Gnecco, GS
Sanguineti, M
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)
Date
2024
Size
File type(s)
Location
License
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.

Journal Title

IEEE Transactions on Neural Networks and Learning Systems

Conference Title
Book Title
Edition
Volume

35

Issue

4

Thesis Type
Degree Program
School
Publisher link
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement

This work is covered by copyright. You must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a specified licence, refer to the licence for details of permitted re-use. If you believe that this work infringes copyright please make a copyright takedown request using the form at https://www.griffith.edu.au/copyright-matters.

Item Access Status
Note
Access the data
Related item(s)
Subject
Persistent link to this record
Citation

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

Collections