Graph Neural Network-Driven Hierarchical Mining for Complex Imbalanced Data

2025-02-06

AI Summary

To address the weak modeling capability and insufficient minority-class pattern capture in high-dimensional imbalanced data, this paper proposes a deep graph-structured representation framework integrated with hierarchical pattern mining. First, a structured graph is constructed to explicitly model high-order dependencies among samples. Then, a deep graph neural network is designed to learn global embeddings, augmented by a hierarchical feature extraction mechanism that enhances local minority-class pattern identification and relational analysis. This work presents the first method that deeply unifies deep graph representation learning with hierarchical imbalance-aware pattern mining. Extensive experiments on multiple benchmark datasets demonstrate significant improvements: a 32.7% increase in minority-class coverage, a 41.5% rise in the number of discriminative patterns discovered, and a 28.3% improvement in average support—outperforming state-of-the-art baseline methods.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Rule Mining & Pattern Mining
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Bridging structured and unstructured data
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

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.

Problem

• Handles high-dimensional imbalanced data
• Improves minority class feature extraction
• Enhances global sample interdependencies analysis

Innovation

• Graph Neural Network embeddings
• Hierarchical mining strategy
• Depth graph model integration