mlsanityData to 14 Oct 2026

Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks

arXiv 2410.17617 ↗First posted 23 Oct 2024cs.LGcited by 33

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Abstract (arXiv)

This paper explores the applications and challenges of graph neural networks (GNNs) in processing complex graph data brought about by the rapid development of the Internet. Given the heterogeneity and redundancy problems that graph data often have, traditional GNN methods may be overly dependent on the initial structure and attribute information of the graph, which limits their ability to accurately simulate more complex relationships and patterns in the graph. Therefore, this study proposes a graph neural network model under a self-supervised learning framework, which can flexibly combine different types of additional information of the attribute graph and its nodes, so as to better mine the deep features in the graph data. By introducing a self-supervisory mechanism, it is expected to improve the adaptability of existing models to the diversity and complexity of graph data and improve the overall performance of the model.

Key facts. Cited by 33 papers in the map (data through 14 Oct 2026). Builds on 14 of its 21 references that are in the map. First posted on arXiv 23 Oct 2024.

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14 of 21 references.

References · 14

  1. 2024Survival Prediction Across Diverse Cancer Types Using Neural Networkscited by 80
  2. 2023Improving Depth Gradient Continuity in Transformers: A Comparative Study on Monocular Depth Estimation with CNNcited by 35
  3. 2024Dynamic Fraud Detection: Integrating Reinforcement Learning into Graph Neural Networkscited by 19
  4. 2024Adaptive Friction in Deep Learning: Enhancing Optimizers with Sigmoid and Tanh Functioncited by 16
  5. 2024Attention Mechanism and Context Modeling System for Text Mining Machine Translationcited by 15
  6. 2024A Lightweight GAN-Based Image Fusion Algorithm for Visible and Infrared Imagescited by 11
  7. 2024Applying Conditional Generative Adversarial Networks for Imaging Diagnosiscited by 9
  8. 2024Unveiling the Potential of Graph Neural Networks in SME Credit Risk Assessmentcited by 9
  9. 2024Contrastive Learning for Knowledge-Based Question Generation in Large Language Modelscited by 8
  10. 2024Applying Hybrid Graph Neural Networks to Strengthen Credit Risk Analysiscited by 8
  11. 2024Convolutional Neural Networks for Predictive Modeling of Lung Diseasecited by 7
  12. 2024Enhancing Deep Learning with Optimized Gradient Descent: Bridging Numerical Methods and Neural Network Trainingcited by 7
2 more
  1. 2024Optimizing News Text Classification with Bi-LSTM and Attention Mechanism for Efficient Data Processingcited by 7
  2. 2024Graph Neural Network Framework for Sentiment Analysis Using Syntactic Featurecited by 6

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Citing papers per year: 2024: 20; 2025: 13.

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  1. 2024Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generationcited by 13
  2. 2024Emotion-Aware Interaction Design in Intelligent User Interface Using Multi-Modal Deep Learningcited by 13
  3. 2024Adaptive Optimization for Enhanced Efficiency in Large-Scale Language Model Trainingcited by 13
  4. 2024Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classificationcited by 11
  5. 2025Graph Neural Network-Driven Hierarchical Mining for Complex Imbalanced Datacited by 11
  6. 2024Graph Neural Network-Based Entity Extraction and Relationship Reasoning in Complex Knowledge Graphscited by 9
  7. 2024Enhancing Few-Shot Learning with Integrated Data and GAN Model Approachescited by 9
  8. 2024Deep Learning for Cross-Border Transaction Anomaly Detection in Anti-Money Laundering Systemscited by 9
  9. 2024Leveraging Convolutional Neural Network-Transformer Synergy for Predictive Modeling in Risk-Based Applicationscited by 9
  10. 2025Social Network User Profiling for Anomaly Detection Based on Graph Neural Networkscited by 9
  11. 2024Machine Learning Techniques for Pattern Recognition in High-Dimensional Data Miningcited by 8
  12. 2025Context-Guided Dynamic Retrieval for Improving Generation Quality in RAG Modelscited by 8
21 more
  1. 2024A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generationcited by 7
  2. 2024Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Datacited by 7
  3. 2024AI-Driven Health Monitoring of Distributed Computing Architecture: Insights from XGBoost and SHAPcited by 7
  4. 2025Context-Aware Rule Mining Using a Dynamic Transformer-Based Frameworkcited by 7
  5. 2025A Deep Learning Framework for Sequence Mining with Bidirectional LSTM and Multi-Scale Attentioncited by 7
  6. 2024Advanced Risk Prediction and Stability Assessment of Banks Using Time Series Transformer Modelscited by 6
  7. 2024Dynamic Scheduling Strategies for Resource Optimization in Computing Environmentscited by 6
  8. 2024Optimizing Multi-Task Learning for Enhanced Performance in Large Language Modelscited by 5
  9. 2024Collaborative Optimization in Financial Data Mining Through Deep Learning and ResNeXtcited by 5
  10. 2024Feature Alignment-Based Knowledge Distillation for Efficient Compression of Large Language Modelscited by 5
  11. 2025Multi-Level Attention and Contrastive Learning for Enhanced Text Classification with an Optimized Transformercited by 5
  12. 2025A LongFormer-Based Framework for Accurate and Efficient Medical Text Summarizationcited by 5
  13. 2025Deep Learning Approach for Clinical Risk Identification Using Transformer Modeling of Heterogeneous EHR Datacited by 5
  14. 2024Optimizing Large Language Models with an Enhanced LoRA Fine-Tuning Algorithm for Efficiency and Robustness in NLP Taskscited by 4
  15. 2025Dynamic Adaptation of LoRA Fine-Tuning for Efficient and Task-Specific Optimization of Large Language Modelscited by 4
  16. 2025Pre-trained Language Models and Few-shot Learning for Medical Entity Extractioncited by 4
  17. 2025Behavioral Anomaly Detection in Distributed Systems via Federated Contrastive Learningcited by 4
  18. 2025Unified Representation Learning for Multi-Intent Diversity and Behavioral Uncertainty in Recommender Systemscited by 4
  19. 2024Computer Vision-Driven Gesture Recognition: Toward Natural and Intuitive Human-Computercited by 3
  20. 2025A Deep Learning Approach to Anomaly Detection in High-Frequency Trading Datacited by 3
  21. 2024Dynamic User Interface Generation for Enhanced Human-Computer Interaction Using Variational Autoencoderscited by 2