sHGCN: Simplified hyperbolic graph convolutional neural networks

TL;DR

This paper introduces sHGCN, a simplified hyperbolic graph convolutional neural network that enhances computational efficiency and accuracy for modeling hierarchical data structures.

Contribution

The paper proposes a streamlined approach to hyperbolic neural networks, reducing complexity and improving performance and speed.

Findings

Significant improvements in runtime efficiency.

Enhanced predictive accuracy over traditional hyperbolic models.

Broader applicability due to simplified operations.

Abstract

Hyperbolic geometry has emerged as a powerful tool for modeling complex, structured data, particularly where hierarchical or tree-like relationships are present. By enabling embeddings with lower distortion, hyperbolic neural networks offer promising alternatives to Euclidean-based models for capturing intricate data structures. Despite these advantages, they often face performance challenges, particularly in computational efficiency and tasks requiring high precision. In this work, we address these limitations by simplifying key operations within hyperbolic neural networks, achieving notable improvements in both runtime and performance. Our findings demonstrate that streamlined hyperbolic operations can lead to substantial gains in computational speed and predictive accuracy, making hyperbolic neural networks a more viable choice for a broader range of applications.