ergm 4: Computational Improvements

(2) Penn State University; (3) University of Washington); Chad Klumb (3) ((1) University of New South Wales; David R. Hunter (2); Martina Morris (3); Pavel N. Krivitsky (1)

ergm 4: Computational Improvements

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arxiv 2203.08198 v1 pith:LQNWMPGL submitted 2022-03-15 stat.CO

Pavel N. Krivitsky (1) , David R. Hunter (2) , Martina Morris (3) , Chad Klumb (3) ((1) University of New South Wales , (2) Penn State University , (3) University of Washington) This is my paper · ORCID

classification stat.CO

keywords ergmanalysisimprovementsnetworkperformancestatisticalalgorithmsanchors

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The ergm package supports the statistical analysis and simulation of network data. It anchors the statnet suite of packages for network analysis in R introduced in a special issue in Journal of Statistical Software in 2008. This article provides an overview of the performance improvements in the 2021 release of ergm version 4. These include performance enhancements to the Markov chain Monte Carlo and maximum likelihood estimation algorithms as well as broader and faster searching for networks with certain target statistics using simulated annealing.

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Improving exponential-family random graph models for bipartite networks
stat.ME 2025-02 conditional novelty 7.0 of 10

New node-oriented weighted four-cycle statistics for bipartite ERGMs are defined, implemented, and shown in simulation to avoid phase transitions exhibited by existing alternating two-path terms.

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