Hyperspherical Graph Representation Learning with Adaptive Alignment and Uniformity
Published:Dec 30, 2025 08:11
•1 min read
•ArXiv
Analysis
This paper introduces HyperGRL, a novel framework for graph representation learning that avoids common pitfalls of existing methods like over-smoothing and instability. It leverages hyperspherical embeddings and a combination of neighbor-mean alignment and uniformity objectives, along with an adaptive balancing mechanism, to achieve superior performance across various graph tasks. The key innovation lies in the geometrically grounded, sampling-free contrastive objectives and the adaptive balancing, leading to improved representation quality and generalization.
Key Takeaways
- •Proposes HyperGRL, a novel framework for graph representation learning.
- •Employs hyperspherical embeddings with neighbor-mean alignment and uniformity objectives.
- •Introduces an entropy-guided adaptive balancing mechanism for stable training.
- •Achieves superior performance on node classification, clustering, and link prediction tasks.
- •Avoids complex architectures, negative sampling, and sensitive hyperparameter tuning.
Reference
“HyperGRL delivers superior representation quality and generalization across diverse graph structures, achieving average improvements of 1.49%, 0.86%, and 0.74% over the strongest existing methods, respectively.”