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Mathematics
Special Issues
New Advances in Graph Neural Networks (GNNs) and Applications
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New Advances in Graph Neural Networks (GNNs) and Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 15 April 2026 | Viewed by 706

Special Issue Editor

Prof. Dr. Hua Duan

E-Mail Website
Guest Editor
College of Mathematics and System Science, Shandong University of Science and Technology, Qingdao 266590, China
Interests: deep learning; graph neural networks; machine learning; applied mathematics; computer vision; process mining

Special Issue Information

Dear Colleagues,

We sincerely invite you to submit your latest research achievements on Graph neural networks (GNNS) and their various applications, especially those focusing on the topic of "New Advances and Application Practices". This special issue is titled "New Advances in Graph Neural Networks and Applications".

In recent years, graph neural networks, as a powerful tool, have shown great potential in handling complex relational data and have been widely applied in multiple fields such as social network analysis, recommendation systems, bioinformatics, transportation networks, and intelligent perception. GNN can effectively capture the hidden information in large-scale heterogeneous graphs by learning the representations of nodes, edges and substructures, promoting breakthroughs in many key tasks. However, to achieve its efficient deployment and large-scale application in practical scenarios, there are still many challenges.

The main difficulties include: high computing costs, the adaptability of the model in resource-constrained environments, the defense ability against attacks, and the performance stability in dynamic and heterogeneous graphs. For this reason, the hot research directions include algorithm optimization, model compression, distributed training and robustness improvement, etc.

This special issue aims to present the latest technological breakthroughs, innovative applications and future research directions in the field of Graph Neural Networks. We welcome original research papers, review articles and case analyses, aiming to promote the joint exploration of the future development of GNN by the academic and industrial sectors.

We look forward to your wonderful submission and jointly usher in a new era of graph neural networks with global peers!

Prof. Dr. Hua Duan
Guest Editor

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 2600 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 network
  • machine learning
  • applied mathematics
  • computer vision
  • artificial intelligence

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Published Papers (1 paper)

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Research

31 pages, 2025 KB  
Article
Enterprise Bankruptcy Prediction Model Based on Heterogeneous Graph Neural Network for Fusing External Features and Internal Attributes
by Xinke Du, Jinfei Cao, Xiyuan Jiang, Jianyu Duan, Zhen Tian and Xiong Wang
Mathematics 2025, 13(17), 2755; https://doi.org/10.3390/math13172755 - 27 Aug 2025
Viewed by 503
Abstract
Enterprise bankruptcy prediction is a critical task in financial risk management. Traditional methods, such as logistic regression and decision trees, rely heavily on structured financial data, which limits their ability to capture complex relational networks and unstructured industry information. Heterogeneous graph neural networks [...] Read more.
Enterprise bankruptcy prediction is a critical task in financial risk management. Traditional methods, such as logistic regression and decision trees, rely heavily on structured financial data, which limits their ability to capture complex relational networks and unstructured industry information. Heterogeneous graph neural networks (HGNNs) offer a solution by modeling multiple relationships between enterprises. However, current models struggle with financial risk graph data challenges, such as the oversimplification of internal financial features and the lack of dynamic imputation for missing external topological features. To address these issues, we propose HGNN-EBP, an enterprise bankruptcy prediction algorithm that integrates both internal and external features. The model constructs a multi-relational heterogeneous graph that combines structured financial data, unstructured textual information, and real-time industry data. A multi-scale graph convolution network captures diverse relationships, while a Transformer-based self-attention mechanism dynamically imputes missing external topological features. Finally, a multi-layer perceptron (MLP) predicts bankruptcy probability. Experimental results on a dataset of 32,459 Chinese enterprises demonstrate that HGNN-EBP outperforms traditional models, especially in handling relational diversity, missing features, and dynamic financial risk data. Full article
(This article belongs to the Special Issue New Advances in Graph Neural Networks (GNNs) and Applications)
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