Mixing patterns and individual differences in networks

TL;DR

This paper investigates how individual differences influence mixing patterns in networks, introducing a model and methods to quantify and infer these preferences from empirical data.

Contribution

It presents a novel network model that captures individual variation in mixing preferences and provides methods for fitting and inferring these patterns from data.

Findings

Introduced a model capturing individual differences in mixing patterns.

Developed metrics to quantify mean and variation of mixing preferences.

Provided inference methods using maximum likelihood and Bayesian approaches.

Abstract

We study mixing patterns in networks, meaning the propensity for nodes of different kinds to connect to one another. The phenomenon of assortative mixing, whereby nodes prefer to connect to others that are similar to themselves, has been widely studied, but here we go further and examine how and to what extent nodes that are otherwise similar can have different preferences. Many individuals in a friendship network, for instance, may prefer friends who are roughly the same age as themselves, but some may display a preference for older or younger friends. We introduce a network model that captures this behavior and a method for fitting it to empirical network data. We propose metrics to characterize the mean and variation of mixing patterns and show how to infer their values from the fitted model, either using maximum-likelihood estimates of model parameters or in a Bayesian framework that does not require fixing any parameters.