用于图压缩的双曲嵌入
Hyperbolic embeddings for graph compression
AI总结：
研究基于双曲嵌入器提出快速无损图压缩算法，利用双曲几何对无标度网络建模的优势，经实验验证，该算法在真实世界图压缩上比现有技术表现更优，最多可高出42%。
AI中文摘要：
网络理论家假设现实世界网络的结构有几何起源。特别是，双曲几何在表示和建模无标度网络方面很有见解。嵌入器是用于找到网络几何表示的算法。本研究中，我们基于现代双曲嵌入器引入了一种快速无损图压缩算法。在真实世界和生成网络上的实验验证表明，我们的算法在真实世界图上比现有技术最多高出42%。
英文摘要：
Network theoreticians hypothesize that the structure of real-world networks has a geometric origin. Especially, hyperbolic geometry was proven insightful in representing and modeling of scale-free networks. Embedders are algorithms used to find a geometric representation of a network. In this study, we introduce a fast lossless graph compression algorithm based on modern hyperbolic embedders. Experimental validation on real-world and generated networks shows that our algorithm beats state-of-the-art by up to 42% on real-world graphs.
