Graph Machine Learning for Analyzing Complex Networks
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".
Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 2590
Special Issue Editors
Interests: graph data mining; graph machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Graph Machine learning (GML) is widely applied in a variety of complex network analysis scenarios in the real world, such as network public opinion analysis, new crown virus spread, e-commerce search and recommendation, the latter of which has demonstrated powerful representation learning and prediction capabilities. With the advance of deep learning, particularly GML and graphs, neural networks significantly promote the theory of machine learning and encourage industrial research in a variety of academic areas. This Special Issue primarily focuses on the most recent advances in the models, algorithms, theories, and applications of GML, both in academic and industrial fields. Contributions to this Special Issue may include, but are not limited to, research on progress in the following areas: graph classification, data augmentation, scalability, explainability, oversmoothing, heterophily, spectral methods, expressivity, temporal data, generative models, graph transformers, self-supervision, contrastive learning, adversarial attacks/ robustness, recommender systems, molecules, proteins, and other related theoretical and applied research.
Our goal is to stimulate continuous development in these areas. In order to achieve this objective, we invite authors to submit original research articles, as well as high-quality review articles that reflect the theme of this Special Issue.
Dr. Dongxiao He
Prof. Dr. Xiankun Zhang
Guest Editors
Manuscript Submission Information
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Keywords
- graph machine learning
- complex network analysis
- deep learning
- graph neural networks