Graph and Geometric Deep Learning
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 October 2024 | Viewed by 314
Special Issue Editors
Interests: pattern recognition; machine learning; deep learning; graph neural networks and their applications
Interests: Machine Learning; Pattern Recognition; Computer Vision
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Graph Neural Networks (GNNs) have risen in popularity and have become pivotal in interpreting the rich data encoded in graph structures.
This Special Issue aims to delve into pioneering GNN methodologies and their expansive applications, which are redefining the limits of artificial intelligence.
We welcome contributions that confront challenges such as over-smoothing, scalability, and generalization, particularly emphasizing transfer learning and few-shot learning within the realm of graph domains. Moreover, we are particularly interested in submissions that demonstrate the beneficial application of GNNs in diverse fields, including but not limited to bioinformatics, social network analysis, recommendation systems, and computer vision. Submissions could also address advancements in the interpretability and explainability of GNNs, which are crucial for their integration into areas where decision-making is sensitive and outcomes are critical.
Finally, considering the recent growth in generative AI, this call for papers also seeks contributions focused on generative models harnessing graphs.
Dr. Alessandro Rozza
Dr. Lorenzo Seidenari
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- graph neural networks
- graph representation learning
- geometric deep learning
