Topology and Geometry of the Third-Party Domains Ecosystem: Measurement and Applications

TL;DR

This paper analyzes the complex network structure of third-party web domains, revealing underlying hyperbolic geometry, and demonstrates how this understanding can predict domain co-hosting, mergers, and organizational relationships.

Contribution

It introduces a novel hyperbolic geometric model of third-party domains, enabling insights into their organization and predictive inference of future domain relationships.

Findings

The third-party domain network exhibits power-law, clustering, and small-world properties.

Hyperbolic geometry effectively models the ecosystem's topology.

The model predicts domain co-hosting, mergers, and organizational links.

Abstract

Over the years, web content has evolved from simple text and static images hosted on a single server to a complex, interactive and multimedia-rich content hosted on different servers. As a result, a modern website during its loading time fetches content not only from its owner's domain but also from a range of third-party domains providing additional functionalities and services. Here, we infer the network of the third-party domains by observing the domains' interactions within users' browsers from all over the globe. We find that this network possesses structural properties commonly found in complex networks, such as power-law degree distribution, strong clustering, and small-world property. These properties imply that a hyperbolic geometry underlies the ecosystem's topology. We use statistical inference methods to find the domains' coordinates in this geometry, which abstract how popular and similar the domains are. The hyperbolic map we obtain is meaningful, revealing the large-scale organization of the ecosystem. Furthermore, we show that it possesses predictive power, providing us the likelihood that third-party domains are co-hosted; belong to the same legal entity; or merge under the same entity in the future in terms of company acquisition. We also find that complementarity instead of similarity is the dominant force driving future domains' merging. These results provide a new perspective on understanding the ecosystem's organization and performing related inferences and predictions.