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Special Issue "Trustworthy Graph Neural Networks: Models and Applications"
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".
Deadline for manuscript submissions: 31 October 2023 | Viewed by 10521
Special Issue Editors
Interests: data mining; machine learning; artificial intelligence; information retrieval; social networks
Special Issue Information
In the era of big data, graph data has attracted considerable attention. We have witnessed the impressive performance of graph neural networks (GNNs) in dealing with graph data, as well as various real-world applications (e.g., recommender systems, molecular property prediction). The increasing number of works on GNNs indicates a global trend in both academic and industrial communities. Despite the progress made in GNNs, there are various open, unexplored, and unidentified challenges. One major concern is whether current GNNs are trustworthy. This is an inescapable problem when GNNs step into real-world applications, especially in risk-sensitive domains. To address this problem, we need to make GNNs more robust, explainable, and stable. Thus, there is a pressing demand for novel and advanced trustworthy GNNs. In this Special Issue, our goal is to bring together researchers and practitioners working in the areas of GNNs to address a wide range of theoretical and practical issues.
Prof. Dr. Zhao Kang
Prof. Dr. Xiao Wang
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. Mathematics 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 2100 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.
- graph neural networks
- deep learning for graphs
- graph representation learning
- spectral graph theory
- robust graph neural networks
- explainable graph neural networks
- stable graph neural networks
- uncertainty in graph neural networks
- graph neural networks related applications