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Mathematics
Special Issues
Machine Learning and Graph Neural Networks
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Machine Learning and 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: 31 October 2026 | Viewed by 2716

Special Issue Editor

Prof. Dr. Shuai Zhao

E-Mail Website
Guest Editor
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: distributed deep learning; Internet of Things; graph neural networks

Special Issue Information

Dear Colleagues,

The research field of machine learning for graphs studies the application of well-known machine learning concepts to the processing of graph-structured data. Graphs are abstract objects that naturally represent interacting systems of entities, where interactions denote the functional and/or structural dependencies between them. Graph Neural Networks are a recent family of machine learning models specifically designed to harness the inherent structure and dependencies present in graph-structured data, revolutionizing the way we analyze, model, and make predictions in complex networked structures. The Special Issue aims to cover applications where machine learning and Graph Neural Networks have proven to be effective. We invite authors from academia and industry to contribute their original research articles, surveys, and high-quality review papers that demonstrate the effectiveness of machine learning and Graph Neural Networks in solving real-world problems while showcasing the latest developments and novel applications. The special session is an excellent opportunity for the machine learning community to gather together and host novel ideas, showcase potential applications, and discuss the new directions of this remarkably successful research field.

We encourage submissions that address, but are not limited to, the following areas:

  • Machine learning;
  • Graph Embeddings;
  • Deep Learning on graphs (Graph Convolutions, Graph Attention Networks, Graph Autoencoders, and Graph Spatial–Temporal Networks);
  • Learning on dynamic, temporal, and/or complex graphs;
  • Knowledge modeling/representation in/for graph learning;
  • Novel models and algorithms for graphs;
  • Node/graph Classification/prediction;
  • Wired/Wireless Communication Networks;
  • Internet of Things;
  • Natural Language Processing;
  • Computer Vision;
  • Recommendation Systems;
  • Other areas (Mobility/Transportation, Geographical, Financial, and Robotics/Cyber–physical).

Prof. Dr. Shuai Zhao
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

  • machine learning
  • graph neural networks
  • deep learning
  • Internet of Things
  • computer vision

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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24 pages, 2427 KB  
Article
ReDyGait: Representation Disentanglement with Gated Attention for Invariant-Contextual Transfer in Stance Detection
by Yanzhou Ma, Yun Luo and Mingyang Peng
Mathematics 2026, 14(7), 1237; https://doi.org/10.3390/math14071237 - 7 Apr 2026
Viewed by 410
Abstract
Cross-topic stance detection degrades when encoders entangle stance signals with topic-specific vocabulary, causing representations that fail to transfer to unseen targets. Existing methods commit to either topic-invariant or topic-aware representations and apply the same strategy uniformly to every input, sacrificing complementary information. We [...] Read more.
Cross-topic stance detection degrades when encoders entangle stance signals with topic-specific vocabulary, causing representations that fail to transfer to unseen targets. Existing methods commit to either topic-invariant or topic-aware representations and apply the same strategy uniformly to every input, sacrificing complementary information. We propose ReDyGait, a three-stage framework that disentangles these two types of signals through dedicated contrastive pre-training and recombines them adaptively at inference time. Stage 1 trains a topic-invariant encoder with supervised contrastive loss over cross-topic positives. Stage 2 trains a topic-contextual encoder with bidirectional pair contrastive loss over within-topic positives; both stages employ topic-aware hard negative mining to prevent shortcut learning. Stage 3 freezes the two contrastive encoders and learns a gating network that produces per-instance weights over invariant, contextual, and base-encoder pathways. On VAST, ReDyGait achieves a macro-averaged F1 of 0.782 in the zero-shot setting and 0.752 in the few-shot setting, improving over the strongest baseline by 1.1 points in both; on SEM16t6 in a leave-one-target-out setup, ReDyGait reaches an average F1 of 0.612. Analysis of the learned gate weights shows that the model shifts toward the invariant pathway for unfamiliar topics and toward the contextual pathway when topic-specific patterns are available, confirming that the disentanglement operates as intended. Full article
(This article belongs to the Special Issue Machine Learning and Graph Neural Networks)
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14 pages, 6712 KB  
Article
An Adaptive Sticky Hidden Markov Model for Robust State Inference in Non-Stationary Physiological Time Series
by Qizheng Wang, Yuping Wang, Shuai Zhao, Yuhan Wu and Shengjie Li
Mathematics 2026, 14(7), 1107; https://doi.org/10.3390/math14071107 - 25 Mar 2026
Viewed by 544
Abstract
The accurate inference of hidden states from non-stationary physiological signals remains a significant challenge in stochastic process modeling. This paper proposes an Adaptive Sticky Hidden Markov Model (Sticky-HMM) framework designed to enhance the robustness of state decoding in noisy environments. To address the [...] Read more.
The accurate inference of hidden states from non-stationary physiological signals remains a significant challenge in stochastic process modeling. This paper proposes an Adaptive Sticky Hidden Markov Model (Sticky-HMM) framework designed to enhance the robustness of state decoding in noisy environments. To address the “state-flickering” issue inherent in traditional HMMs, we incorporate a “Sticky” parameter into the transition matrix, imposing a temporal penalty on spurious state switching to maintain continuity. Furthermore, we introduce a Dynamic Prior Strategy that adaptively calibrates self-transition probabilities by mapping frequency-domain features of the observed sequence to the model’s parameter space. The proposed decoding process employs a two-pass refinement strategy and the Viterbi algorithm in the logarithmic domain to ensure numerical stability. The model’s efficacy was validated using a high-fidelity dataset of simulated apnea events. This work provides a computationally efficient and mathematically rigorous approach that demonstrates strong potential for long-term respiratory health monitoring. Full article
(This article belongs to the Special Issue Machine Learning and Graph Neural Networks)
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47 pages, 3245 KB  
Article
DISPEL-GNN: De-Illusion via Spectral Stability and Perturbation Bound-Enforced Learning for Community Detection with Risk-Aware Dynamic Attention in Graph Neural Networks
by Daozheng Qu, Yanfei Ma and Mykhailo Pyrozhenko
Mathematics 2026, 14(4), 602; https://doi.org/10.3390/math14040602 - 9 Feb 2026
Cited by 1 | Viewed by 812
Abstract
Community detection in graphs can be viewed as the estimation of a partition map that remains stable under admissible perturbations of graph topology and node attributes. While modern graph neural networks (GNNs) achieve strong empirical accuracy, they often exhibit severe assignment drift under [...] Read more.
Community detection in graphs can be viewed as the estimation of a partition map that remains stable under admissible perturbations of graph topology and node attributes. While modern graph neural networks (GNNs) achieve strong empirical accuracy, they often exhibit severe assignment drift under minor perturbations, leading to illusory community structures. In this work, we propose DISPEL-GNN, a stability-aware graph learning framework that integrates spectral operator regularization, Bayesian uncertainty modeling, and risk-aware dynamic attention for perturbation-bounded community detection. The model explicitly constrains graph operators through uniform spectral norm bounds, high-frequency energy suppression, and commutator alignment while dynamically modulating message passing based on node-level spectral risk and epistemic uncertainty. We further formalize instability via assignment of drift functional and establish perturbation bounds linking drift to operator norms and spectral gaps, complemented by a PAC-Bayesian generalization guarantee. Extensive experiments on real-world benchmarks including Cora, Citeseer, Pubmed, Cora-Full, and DBLP demonstrate that DISPEL-GNN consistently reduces assignment drift by 18–35% under feature noise and edge perturbations while improving clustering quality with up to +3.0 NMI and +0.04 ARI compared to strong baselines such as GAT and Bayesian GNNs. The normalized mutual information (NMI), adjusted Rand index (ARI), and PAC-Bayesian (PAC) constraints serve as evaluative and theoretical instruments in this study. Additional studies on synthetic graphs with controlled spectral gaps confirm that the proposed method maintains stable community assignments in low-gap regimes where classical spectral and GNN-based methods degrade sharply. These results establish DISPEL-GNN as a mathematically grounded and practically effective framework for robust and interpretable community detection. A metric-wise dominance analysis shows that DISPEL-GNN achieves metric-wise dominance across most accuracy and robustness criteria, with minor tradeoffs in modularity on selected datasets. These results indicate that explicitly modeling stability and uncertainty provides a principled pathway toward reliable and interpretable community detection in noisy graph environments. Full article
(This article belongs to the Special Issue Machine Learning and Graph Neural Networks)
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