Exponential random graph model parameter estimation for very large directed networks

Autoři: Alex Stivala aff001;  Garry Robins aff003;  Alessandro Lomi aff001
Působiště autorů: Institute of Computational Science, Università della Svizzera italiana, Lugano, Ticino, Switzerland aff001;  Centre for Transformative Innovation, Swinburne University of Technology, Melbourne, Victoria, Australia aff002;  Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Victoria, Australia aff003;  The University of Exeter Business School, The University of Exeter, Exeter, Devon, United Kingdom aff004
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0227804


Exponential random graph models (ERGMs) are widely used for modeling social networks observed at one point in time. However the computational difficulty of ERGM parameter estimation has limited the practical application of this class of models to relatively small networks, up to a few thousand nodes at most, with usually only a few hundred nodes or fewer. In the case of undirected networks, snowball sampling can be used to find ERGM parameter estimates of larger networks via network samples, and recently published improvements in ERGM network distribution sampling and ERGM estimation algorithms have allowed ERGM parameter estimates of undirected networks with over one hundred thousand nodes to be made. However the implementations of these algorithms to date have been limited in their scalability, and also restricted to undirected networks. Here we describe an implementation of the recently published Equilibrium Expectation (EE) algorithm for ERGM parameter estimation of large directed networks. We test it on some simulated networks, and demonstrate its application to an online social network with over 1.6 million nodes.

Klíčová slova:

Algorithms – Directed graphs – Network analysis – Network reciprocity – Random graphs – Research errors – Social networks – Statistical distributions


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