Why social networks are different from other types of networks

TL;DR

This paper explains why social networks are unique by highlighting their high clustering and positive degree correlations, and demonstrates how group structures can account for these features.

Contribution

It introduces a simple model linking group structures to degree correlations, explaining the distinct properties of social networks.

Findings

Social networks exhibit high clustering and assortative mixing.

Group size variation explains degree correlations in social networks.

Model predictions align with real-world network observations.

Abstract

We argue that social networks differ from most other types of networks, including technological and biological networks, in two important ways. First, they have non-trivial clustering or network transitivity, and second, they show positive correlations, also called assortative mixing, between the degrees of adjacent vertices. Social networks are often divided into groups or communities, and it has recently been suggested that this division could account for the observed clustering. We demonstrate that group structure in networks can also account for degree correlations. We show using a simple model that we should expect assortative mixing in such networks whenever there is variation in the sizes of the groups and that the predicted level of assortative mixing compares well with that observed in real-world networks.