Hypergraph-Based High-Order Correlation Analysis for Large-Scale Long-Tailed Data Classification.

Area of Science:

• Machine Learning
• Data Mining
• Network Science

Background:

• High-order correlations capture complex multi-entity interactions beyond traditional graphs.
• Existing neural network models struggle with scalability and computational complexity for large-scale high-order data.
• Long-tailed distributions in real-world data lead to underrepresented categories, hindering pattern learning for rare instances.

Purpose of the Study:

• To develop a novel framework for analyzing high-order correlations in large-scale, long-tailed datasets.
• To improve the representation of underrepresented categories in imbalanced datasets.
• To enhance the computational efficiency of high-order correlation analysis.

Main Methods:

• Introduced HyperGraph-based High-order Correlation analysis (HGHC) framework.
• Developed an oversampling module (HSMOTE) to generate synthetic vertices and enhance tail category representation.
• Implemented a dual-modality approach (structural and feature) with separate CPU/GPU computations for efficient scaling.
• Created the Amazon-LT benchmark dataset for large-scale, long-tailed high-order correlation analysis.

Main Results:

• HGHC effectively addresses the challenges of long-tailed distributions by enhancing tail category representation.
• The dual-modality computation and fusion scheme significantly improves computational scalability.
• HGHC achieves state-of-the-art performance on the new Amazon-LT benchmark and other large-scale, long-tailed datasets.
• Demonstrated the framework's ability to learn effective high-order interaction patterns for rare instances.

Conclusions:

• HGHC provides an effective and scalable solution for high-order correlation analysis on large-scale, long-tailed data.
• The proposed methods significantly improve the handling of data imbalance and computational complexity.
• The Amazon-LT benchmark facilitates future research in this domain.