A comparative study of social network models: network evolution models and nodal attribute models

TL;DR

This paper compares social network models based on network evolution and nodal attributes, evaluating their ability to replicate real-world social network properties such as degree distribution, clustering, and community structure.

Contribution

It provides a systematic comparison of two main classes of social network models and assesses their realism against empirical data.

Findings

Nodal attribute models produce clear community structures and assortativity.

Network evolution models better match degree distributions and clustering spectra.

ERGMs show weaker community structures.

Abstract

This paper reviews, classifies and compares recent models for social networks that have mainly been published within the physics-oriented complex networks literature. The models fall into two categories: those in which the addition of new links is dependent on the (typically local) network structure (network evolution models, NEMs), and those in which links are generated based only on nodal attributes (nodal attribute models, NAMs). An exponential random graph model (ERGM) with structural dependencies is included for comparison. We fit models from each of these categories to two empirical acquaintance networks with respect to basic network properties. We compare higher order structures in the resulting networks with those in the data, with the aim of determining which models produce the most realistic network structure with respect to degree distributions, assortativity, clustering spectra, geodesic path distributions, and community structure (subgroups with dense internal connections). We find that the nodal attribute models successfully produce assortative networks and very clear community structure. However, they generate unrealistic clustering spectra and peaked degree distributions that do not match empirical data on large social networks. On the other hand, many of the network evolution models produce degree distributions and clustering spectra that agree more closely with data. They also generate assortative networks and community structure, although often not to the same extent as in the data. The ERG model turns out to produce the weakest community structure.