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Information
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Graph Neural Networks and Transformers for Intelligent Data-Driven Systems
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Graph Neural Networks and Transformers for Intelligent Data-Driven Systems

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Processes".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 1800

Special Issue Editor

Dr. Xiaodi Huang

E-Mail Website
Guest Editor
School of Computing, Mathematics, and Engineering, Charles Sturt University, Albury, NSW 2640, Australia
Interests: applied machine leanring; data analysis and visualizaiton; computer applications

Special Issue Information

Dear Colleagues,

This Special Issue invites original research articles on Graph Neural Networks (GNNs) and Transformers for Intelligent Data-Driven Systems. We aim to showcase innovative research advancing theoretical foundations, methodologies, and applications of GNNs and Transformers in areas such as natural language processing, graph-based reasoning, predictive modeling, and other data-driven intelligent systems. The goal is to foster interdisciplinary collaboration, promote knowledge transfer, and highlight technological breakthroughs in these rapidly evolving fields.

We welcome high-quality, original submissions that have not been previously published or are under consideration elsewhere. Contributions should provide novel insights, rigorous methodologies, or impactful applications of GNNs and Transformers.

Dr. Xiaodi Huang
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. Information is an international peer-reviewed open access monthly 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 1800 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
  • transformers
  • intelligent data-driven systems
  • graph representation learning
  • attention mechanisms
  • self-supervised learning
  • graph convolutional networks
  • knowledge representation
  • knowledge discovery
  • sequence modeling
  • graph embeddings
  • deep neural architectures
  • scalable graph algorithms
  • multimodal learning
  • explainable ai
  • temporal graph networks
  • data-driven decision making
  • adaptive learning systems
  • real-time data analytics
  • graph-based decision systems
  • intelligent information processing
  • contextual data modeling
  • dynamic graph learning
  • semantic data integration
  • predictive intelligence
  • autonomous data systems
  • multi-source data fusion

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Published Papers (2 papers)

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Research

21 pages, 1027 KB  
Article
Enhancing MOOC Recommendation Through Preference-Aware Knowledge Graph Diffusion and Temporal Sequence Modeling
by Chao Duan, Wenlong Zhang, Qiaoling Cui, Yu Pei, Bin He and Qionghao Huang
Information 2025, 16(12), 1061; https://doi.org/10.3390/info16121061 - 3 Dec 2025
Viewed by 332
Abstract
Course recommendation is a critical service in Intelligent Tutoring Systems (ITS) that helps learners discover relevant courses from massive online educational platforms. Despite substantial progress in this field, two key challenges remain unresolved: (1) existing methods fail to leverage the differences in learners’ [...] Read more.
Course recommendation is a critical service in Intelligent Tutoring Systems (ITS) that helps learners discover relevant courses from massive online educational platforms. Despite substantial progress in this field, two key challenges remain unresolved: (1) existing methods fail to leverage the differences in learners’ interests across different courses during knowledge propagation processes, and (2) while sequential relationships have been considered in course recommendations, there is still significant room for improvement in effectively integrating sequential patterns with knowledge-graph-based approaches. To overcome these limitations, we propose PGDB (Preference-aware Graph Diffusion network and Bi-LSTM), an innovative end-to-end framework for course recommendation. Our model consists of four key components: First, a course knowledge graph diffusion module recursively collects multiple knowledge triples related to learners to construct their knowledge background. Second, a preference-aware diffusion attention mechanism analyzes learners’ preferences for courses and relational paths using multi-head attention, effectively distinguishing semantic diversity across different contexts and capturing varying learner interests during knowledge transmission. Third, a temporal sequence modeling module utilizes bidirectional long short-term memory networks to identify learners’ interest evolution patterns, generating learner-dependent representations that efficiently leverage sequential relationships between courses. Finally, a prediction module combines the final representations of learners and courses to output selection probabilities for candidate courses. Extensive experimental results demonstrate that PGDB significantly outperforms state-of-the-art baseline models across multiple evaluation metrics, validating the effectiveness of our approach in addressing data sparsity and sequential modeling challenges in course recommendation systems. Full article
(This article belongs to the Special Issue Graph Neural Networks and Transformers for Intelligent Data-Driven Systems)
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15 pages, 930 KB  
Article
Performance Evaluation Metrics for Empathetic LLMs
by Yuna Hong, Bonhwa Ku and Hanseok Ko
Information 2025, 16(11), 977; https://doi.org/10.3390/info16110977 - 11 Nov 2025
Viewed by 1206
Abstract
With the rapid advancement of large language models (LLMs), recent systems have demonstrated increasing capability in understanding and expressing human emotions. However, no objective and standardized metric currently exists to evaluate how empathetic an LLM’s response is. To address this gap, we propose [...] Read more.
With the rapid advancement of large language models (LLMs), recent systems have demonstrated increasing capability in understanding and expressing human emotions. However, no objective and standardized metric currently exists to evaluate how empathetic an LLM’s response is. To address this gap, we propose a novel evaluation framework that measures both sentiment-level and emotion-level alignment between a user query and a model-generated response. The proposed metric consists of two components. The sentiment component evaluates overall affective polarity through Sentlink and the naturalness of emotional expression via NEmpathySort. The emotion component measures fine-grained emotional correspondence using Emosight. Additionally, a semantic component, based on RAGAS, assesses the contextual relevance and coherence of the response. Experimental results demonstrate that our metric effectively captures both the intensity and nuance of empathy in LLM-generated responses, providing a solid foundation for the development of emotionally intelligent conversational AI. Full article
(This article belongs to the Special Issue Graph Neural Networks and Transformers for Intelligent Data-Driven Systems)
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