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Found 4 papers, 4 papers with code
Testing biological network motif significance with exponential random graph models
ERGM models indicate over-representation of triangles in the PPI network, and confirm results from previous research as to over-representation of transitive triangles (feed-forward loop) in an E. coli and a yeast regulatory network.
Exponential random graph model parameter estimation for very large directed networks
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.
Methodology Applications
A Simple Algorithm for Scalable Monte Carlo Inference
The methods of statistical physics are widely used for modelling complex networks.
Fast Maximum Likelihood estimation via Equilibrium Expectation for Large Network Data
We demonstrate the performance of the EE algorithm in the context of exponential random graphmodels (ERGMs) a family of statistical models commonly used in empirical research based on network data observed at a single period in time.
