Spatial Strength Centrality and the Effect of Spatial Embeddings on Network Architecture

TL;DR

This paper introduces a new spatial strength centrality measure to analyze how spatial embeddings influence network structure, extending existing models with Euclidean space embeddings and probabilistic edge formation.

Contribution

It develops spatial versions of popular network models and defines a novel centrality measure to quantify the impact of spatial embeddings on network architecture.

Findings

Spatial strength centrality effectively characterizes the influence of spatial embeddings.

Extended models show that longer edges are less probable, aligning with real-world networks.

Spatial embeddings significantly alter network topology and connectivity patterns.

Abstract

For many networks, it is useful to think of their nodes as being embedded in a latent space, and such embeddings can affect the probabilities for nodes to be adjacent to each other. In this paper, we extend existing models of synthetic networks to spatial network models by first embedding nodes in Euclidean space and then modifying the models so that progressively longer edges occur with progressively smaller probabilities. We start by extending a geographical fitness model by employing Gaussian-distributed fitnesses, and we then develop spatial versions of preferential attachment and configuration models. We define a notion of "spatial strength centrality" to help characterize how strongly a spatial embedding affects network structure, and we examine spatial strength centrality on a variety of real and synthetic networks.