Fast and scalable likelihood maximization for Exponential Random Graph Models with local constraints

Fast and scalable likelihood maximization for Exponential Random Graph Models with local constraints

Abstract Exponential Random Graph Models (ERGMs) have gained increasing popularity over the years. Rooted into statistical physics, the ERGMs framework has been successfully employed for reconstructing networks, detecting statistically significant patterns in graphs, counting networked configuration...

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Bibliographic Details
Main Authors: Nicolò Vallarano, Matteo Bruno, Emiliano Marchese, Giuseppe Trapani, Fabio Saracco, Giulio Cimini, Mario Zanon, Tiziano Squartini
Format: Article
Language:English
Published: Nature Portfolio 2021-07-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-021-93830-4

Internet

https://doi.org/10.1038/s41598-021-93830-4

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