Spatial Growth of Real-world Networks

TL;DR

This paper explores spatial growth mechanisms for real-world networks that account for clustering and scale-free degree distributions without relying on preferential attachment.

Contribution

It introduces spatial growth models that explain network properties like clustering and scale-free distributions without the need for preferential attachment.

Findings

Spatial growth models can produce clustered, scale-free networks.

Networks can have scale-free degree distributions without hubs.

The models explain properties of real-world networks not captured by traditional growth models.

Abstract

Many real-world networks have properties of small-world networks, with clustered local neighborhoods and low average-shortest path (ASP). They may also show a scale-free degree distribution, which can be generated by growth and preferential attachment to highly connected nodes, or hubs. However, many real-world networks consist of multiple, inter-connected clusters not normally seen in systems grown by preferential attachment, and there also exist real-world networks with a scale-free degree distribution that do not contain highly connected hubs. We describe spatial growth mechanisms, not using preferential attachment, that address both aspects.