DDEC: Dual Dependency-Enhanced Contrastive Learning for Sparse Hypergraph Node Classification

Area of Science:

• Graph Neural Networks
• Machine Learning
• Data Mining

Background:

• Hypergraph neural networks excel at capturing complex relationships but struggle with sparse data.
• Sparse hypergraphs limit interaction modeling, hindering attribute propagation and dependency capture.

Purpose of the Study:

• To propose DDEC, a Dual Dependency-Enhanced Contrastive learning framework for node classification in sparse hypergraphs.
• To enhance the modeling of both structural and attribute information for improved node classification accuracy.

Main Methods:

• DDEC introduces an attribute view to complement the structural view, addressing information loss in sparse hypergraphs.
• An entropy-guided feature recalibration mechanism refines attribute interactions by estimating node uncertainty.
• Dual dependency enhancement leverages line graph transformations and attention mechanisms in both structural and attribute views.
• Collaborative contrastive learning at node and hyperedge levels enforces multi-granularity semantic consistency.

Main Results:

• DDEC effectively compensates for relational information loss in sparse hypergraphs.
• The framework successfully captures explicit topological dependencies and latent semantic correlations.
• Adaptive fusion of structural and attribute representations enhances overall performance.

Conclusions:

• DDEC demonstrates superior performance over existing methods for sparse hypergraph node classification.
• The proposed framework is effective and robust across various datasets.
• DDEC offers a promising approach for analyzing complex, sparse relational data.