Sublinear Domination and Core-Periphery Networks

TL;DR

This paper introduces a new generative network model with core-periphery structure where core nodes dominate the network sublinearly, exhibiting power law degree distributions and small-world properties, and fits well to real-world networks.

Contribution

A novel random network model with core nodes acting as sublinear dominators, capturing key properties of real-world networks.

Findings

Generated networks exhibit power law degree distributions.

Model captures small-world phenomena.

Fits various real-world networks effectively.

Abstract

In this paper we devise a generative random network model with core-periphery properties whose core nodes act as sublinear dominators, that is, if the network has $n$ nodes, the core has size $o(n)$ and dominates the entire network. We show that instances generated by this model exhibit power law degree distributions, and incorporates small-world phenomena. We also fit our model in a variety of real-world networks.