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Mathematics
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Advances in Graph Neural Networks
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Advances in Graph Neural Networks

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

Deadline for manuscript submissions: 20 May 2026 | Viewed by 938

Special Issue Editor

Dr. Chaozhuo Li

E-Mail Website
Guest Editor
Key Laboratory of Trustworthy Distributed Computing and Service (MoE), Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: neural networks; recommender systems; data mining; information fusion

Special Issue Information

Dear Colleagues,

Graph-structured data has become ubiquitous in various real-world domains, such as social networks, biological systems, knowledge graphs, and recommendation systems. Traditional machine learning methods struggle to effectively capture the complex topological dependencies and high-dimensional interactions inherent in such data. Graph Neural Networks (GNNs) have emerged, exemplifying manifestations of powerful paradigm for addressing these challenges, enabling deep representation learning directly on graphs. With ongoing developments in architecture design, scalability, interpretability, and robustness, GNNs are rapidly evolving and expanding their impact across disciplines. This Special Issue focuses on advancing the theoretical foundations, computational techniques, and practical applications of GNNs, with the goal of pushing the boundaries of graph-based intelligence.

We invite high-quality submissions that explore innovative methods, frameworks, and applications related to Graph Neural Networks. Topics of interest include, but are not limited to, novel GNN architectures, scalable and efficient GNN training, self-supervised and unsupervised GNN learning, explainability and fairness in graph models, and privacy-preserving GNNs. We also welcome papers applying GNNs in diverse domains such as genomics, cybersecurity, social computing, financial modeling, and recommender systems. This Special Issue encourages interdisciplinary contributions and aims to foster collaboration across machine learning, data mining, network science, and domain-specific communities.

Dr. Chaozhuo Li
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 250 words) can be sent to the Editorial Office for assessment.

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 networks
  • graph representation learning
  • social network analysis

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Published Papers (1 paper)

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Research

24 pages, 704 KB  
Article
Few-Shot Community Detection in Graphs via Strong Triadic Closure and Prompt Learning
by Yeqin Zhou and Heng Bao
Mathematics 2025, 13(19), 3083; https://doi.org/10.3390/math13193083 - 25 Sep 2025
Viewed by 768
Abstract
Community detection is a fundamental task for understanding network structures, crucial for identifying groups of nodes with close connections. However, existing methods generally treat all connections in networks as equally important, overlooking the inherent inequality of connection strengths in social networks, and often [...] Read more.
Community detection is a fundamental task for understanding network structures, crucial for identifying groups of nodes with close connections. However, existing methods generally treat all connections in networks as equally important, overlooking the inherent inequality of connection strengths in social networks, and often require large quantities of labeled data. To address these challenges, we propose a few-shot community detection framework, Strong Triadic Closure Community Detection with Prompt (STC-CDP), which combines the Strong Triadic Closure (STC) principle, Graph Neural Networks, and prompt learning. The STC principle, derived from social network theory, states that if two nodes share strong connections with a third node, they are likely to be connected with each other. By incorporating STC constraints during the pre-training phase, STC-CDP can differentiate between strong and weak connections in networks, thereby more accurately capturing community structures. We design an innovative prompt learning mechanism that enables the model to extract key features from a small number of labeled communities and transfer them to the identification of unlabeled communities. Experiments on multiple real-world datasets demonstrate that STC-CDP significantly outperforms existing state-of-the-art methods under few-shot conditions, achieving higher F1 scores and Jaccard similarity particularly on Facebook, Amazon, and DBLP datasets. Our approach not only improves the precision of community detection but also provides new insights into understanding connection inequality in social networks. Full article
(This article belongs to the Special Issue Advances in Graph Neural Networks)
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