CLOVE: Travelling Salesman's approach to hyperbolic embeddings of complex networks with communities

TL;DR

CLOVE is a novel hyperbolic embedding method for complex networks that hierarchically arranges communities using a TSP-based approach, improving embedding quality and efficiency for machine learning applications.

Contribution

It introduces a hierarchical community-based hyperbolic embedding technique utilizing TSP optimization, outperforming existing methods in quality and computational efficiency.

Findings

Outperforms most alternative embedding methods in quality measures.

Highly computationally efficient for large networks.

Effective for downstream machine learning tasks.

Abstract

The embedding of complex networks into metric spaces has become a research topic of high interest with a wide variety of proposed methods. Low dimensional hyperbolic spaces offer a natural co-domain for embeddings allowing a roughly uniform spatial distribution of the nodes even for scale-free networks and the efficient navigability and estimation of linking probabilities. According to recent results, the communities of a complex network after optimization can be naturally mapped into well-defined angular sectors of the hyperbolic space. Here we introduce CLOVE, an embedding method exploiting this property based on iterative arrangement of the communities in a hierarchical manner, down to individual nodes. A crucial step in the process is finding the optimal angular order of the communities at a given level of the hierarchy, which is solved based on the Travelling Salesman Problem. Since CLOVE outperforms most of the alternative methods regarding different embedding quality measures and is computationally very efficient, it can be very useful in related down-stream machine learning tasks such as AI based pattern recognition.