Friend of a friend models of network growth

TL;DR

This paper introduces a local network growth model based on friend-of-a-friend interactions, which naturally produces power-law degree distributions, clustering, and super-hub networks, aligning well with many real-world networks.

Contribution

It presents a simple, local rule-based network growth model that reproduces key features of complex networks without relying on global information.

Findings

Produces power-law degree distributions with exponents from 1.5 upwards

Generates clustering coefficients up to 0.5, matching real networks

Can create super-hub networks under certain parameters

Abstract

One of the best-known models in network science is preferential attachment. In this model, the probability of attaching to a node depends on the degree of all nodes in the population, and thus depends on global information. In many biological, physical, and social systems, however, interactions between individuals depend only on local information. Here, we investigate a truly local model of network formation, based on the idea of a friend of a friend, with the following rule: individuals choose one node at random and link to it with probability p, then they choose a neighbour of that node and link with probability q. Our model produces power laws with empirical exponents ranging from 1.5 upwards and clustering co-efficients ranging from 0 up to 0.5 (consistent with many real networks). For small p and q=1, the model produces super-hub networks, and we prove that for p=0 and q=1, the proportion of non-hubs tends to 1 as the network grows. We show that power-law degree distributions, small world clustering and super-hub networks are all outcomes of this, more general, yet conceptually simple model.