# Graph Neural Network-Driven Hierarchical Mining for Complex Imbalanced Data

arXiv: https://arxiv.org/abs/2502.03803 · HTML: https://mlsanity.com/paper/2502.03803/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 6 Feb 2025.

## Builds on (most cited first, 12 of 12)

- [Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining](https://mlsanity.com/paper/2409.14327/) (2024)
- [Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks](https://mlsanity.com/paper/2410.17617/) (2024)
- [Object Detection for Medical Image Analysis: Insights from the RT-DETR Model](https://mlsanity.com/paper/2501.16469/) (2025)
- [Leveraging Convolutional Neural Network-Transformer Synergy for Predictive Modeling in Risk-Based Applications](https://mlsanity.com/paper/2412.18222/) (2024)
- [Robust Graph Neural Networks for Stability Analysis in Dynamic Networks](https://mlsanity.com/paper/2411.11848/) (2024)
- [Leveraging Generative Adversarial Networks for Addressing Data Imbalance in Financial Market Supervision](https://mlsanity.com/paper/2412.15222/) (2024)
- [Multi-Level Attention and Contrastive Learning for Enhanced Text Classification with an Optimized Transformer](https://mlsanity.com/paper/2501.13467/) (2025)
- [Few-Shot Learning with Adaptive Weight Masking in Conditional GANs](https://mlsanity.com/paper/2412.03105/) (2024)
- [Adaptive User Interface Generation Through Reinforcement Learning: A Data-Driven Approach to Personalization and Optimization](https://mlsanity.com/paper/2412.16837/) (2024)
- [Computer Vision-Driven Gesture Recognition: Toward Natural and Intuitive Human-Computer](https://mlsanity.com/paper/2412.18321/) (2024)
- [Dynamic User Interface Generation for Enhanced Human-Computer Interaction Using Variational Autoencoders](https://mlsanity.com/paper/2412.14521/) (2024)
- [Exploring Beyond Logits: Hierarchical Dynamic Labeling Based on Embeddings for Semi-Supervised Classification](https://mlsanity.com/paper/2404.17173/) (2024)

## Built on by (most cited first, 11 of 11)

- [Detecting Credit Card Fraud via Heterogeneous Graph Neural Networks with Graph Attention](https://mlsanity.com/paper/2504.08183/) (2025)
- [Context-Aware Rule Mining Using a Dynamic Transformer-Based Framework](https://mlsanity.com/paper/2503.11125/) (2025)
- [Contrastive and Variational Approaches in Self-Supervised Learning for Complex Data Mining](https://mlsanity.com/paper/2504.04032/) (2025)
- [Semantic and Contextual Modeling for Malicious Comment Detection with BERT-BiLSTM](https://mlsanity.com/paper/2503.11084/) (2025)
- [A Data Balancing and Ensemble Learning Approach for Credit Card Fraud Detection](https://mlsanity.com/paper/2503.21160/) (2025)
- [A Deep Learning Framework for Boundary-Aware Semantic Segmentation](https://mlsanity.com/paper/2503.22050/) (2025)
- [Improving Harmful Text Detection with Joint Retrieval and External Knowledge](https://mlsanity.com/paper/2504.02310/) (2025)
- [Detecting High-Potential SMEs with Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2602.19591/) (2026)
- [Adaptive Transformer Attention and Multi-Scale Fusion for Spine 3D Segmentation](https://mlsanity.com/paper/2503.12853/) (2025)
- [GKAN: Explainable Diagnosis of Alzheimer's Disease Using Graph Neural Network with Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2504.00946/) (2025)
- [When Corrective Hints Hurt: Prompt Design in Reasoner-Guided Repair of LLM Overcaution on Entailed Negations under OWL~2~DL](https://mlsanity.com/paper/2604.23398/) (2026)

## Abstract (arXiv)

This study presents a hierarchical mining framework for high-dimensional imbalanced data, leveraging a depth graph model to address the inherent performance limitations of conventional approaches in handling complex, high-dimensional data distributions with imbalanced sample representations. By constructing a structured graph representation of the dataset and integrating graph neural network (GNN) embeddings, the proposed method effectively captures global interdependencies among samples. Furthermore, a hierarchical strategy is employed to enhance the characterization and extraction of minority class feature patterns, thereby facilitating precise and robust imbalanced data mining. Empirical evaluations across multiple experimental scenarios validate the efficacy of the proposed approach, demonstrating substantial improvements over traditional methods in key performance metrics, including pattern discovery count, average support, and minority class coverage. Notably, the method exhibits superior capabilities in minority-class feature extraction and pattern correlation analysis. These findings underscore the potential of depth graph models, in conjunction with hierarchical mining strategies, to significantly enhance the efficiency and accuracy of imbalanced data analysis. This research contributes a novel computational framework for high-dimensional complex data processing and lays the foundation for future extensions to dynamically evolving imbalanced data and multi-modal data applications, thereby expanding the applicability of advanced data mining methodologies to more intricate analytical domains.