Exponential random graph models

TL;DR

This paper provides a theoretical overview of exponential random graph models (ERGMs), emphasizing their analytical foundations and interdisciplinary relevance in complex network science.

Contribution

It offers a more theoretical perspective on ERGMs, aiming to unify understanding across diverse research disciplines in network science.

Findings

Developed analytical techniques for ERGMs

Enhanced theoretical understanding of network modeling

Bridged interdisciplinary approaches in complex networks

Abstract

Nowadays, exponential random graphs (ERGs) are among the most widely-studied network models. Different analytical and numerical techniques for ERG have been developed that resulted in the well-established theory with true predictive power. An excellent basic discussion of exponential random graphs addressed to social science students and researchers is given in [Anderson et al., 1999][Robins et al., 2007]. This essay is intentionally designed to be more theoretical in comparison with the well-known primers just mentioned. Given the interdisciplinary character of the new emerging science of complex networks, the essay aims to give a contribution upon which network scientists and practitioners, who represent different research areas, could build a common area of understanding.