Stylized facts in social networks: Community-based static modeling

TL;DR

This paper introduces a simple, scalable community-based static model that reproduces key stylized facts of social networks, providing a useful benchmark for future research.

Contribution

The paper proposes a novel static modeling approach incorporating heterogeneous community sizes and link density, capturing realistic social network features.

Findings

Model reproduces broad distributions and community structures

Analytical and numerical validation of stylized facts

Simple implementation suitable for benchmarking

Abstract

The past analyses of datasets of social networks have enabled us to make empirical findings of a number of aspects of human society, which are commonly featured as stylized facts of social networks, such as broad distributions of network quantities, existence of communities, assortative mixing, and intensity-topology correlations. Since the understanding of the structure of these complex social networks is far from complete, for deeper insight into human society more comprehensive datasets and modeling of the stylized facts are needed. Although the existing dynamical and static models can generate some stylized facts, here we take an alternative approach by devising a community-based static model with heterogeneous community sizes and larger communities having smaller link density and weight. With these few assumptions we are able to generate realistic social networks that show most stylized facts for a wide range of parameters, as demonstrated numerically and analytically. Since our community-based static model is simple to implement and easily scalable, it can be used as a reference system, benchmark, or testbed for further applications.