A hyperbolic Embedding Model for Directed Networks

TL;DR

This paper introduces a hyperbolic embedding model for directed networks that incorporates asymmetry and bipartite structures, improving the understanding and modeling of complex directed systems like trade and neural networks.

Contribution

It proposes a novel framework that combines topological structure, directed links, and hidden hyperbolic space, addressing asymmetry in network embeddings.

Findings

Directed networks can be effectively embedded in hyperbolic space.

Embedding improves modeling of complex systems like trade and neural networks.

The model enhances existing network analysis applications.

Abstract

Network embedding is a fervid topic in current networks science and observes that most real complex systems can be embedded in hidden metrics space and emerge as the geometrical property, where the geometric distance between nodes determines the likelihood of links connected. Among those, hyperbolic space associated with the structural organization of many real complex systems, it has thus received extensive attention. However, the majority of methods and measurements, recently developed, less take these features into consideration for the asymmetry of links. Here, we discuss how to multiplex node information as an embedding foundation through identifying the bipartite structure of directed networks; and we proposed the generally mapping framework which hybrids the topological structure of complex networks, directed links and the hidden metrics space. By splitting the different properties of a node, possibilities between different types of nodes can be modeled. In addition to that, we apply this model to some real systems, including international trade networks and C.elegans neural networks. Results confirm that directed networks enable mapping into metrics space as well, and network embedding information can improve the scope of application of existing models.