

Estimnet
is a
program for
the statistical analysis of large network data. It computes Maximum
Likelihood estimates of parameters of Exponential
Random Graph Models. Estimnet
is
designed for big data. It is applicable to networks on approximately
1,000 to 100,000 nodes. Please refer to Statnet package or PNet
program if you want to study smaller networks, or to Bergm package
for Bayesian parameter estimation. Publications Alexander Borisenko, Maksym Byshkin, Alessandro Lomi, A Simple Algorithm for Scalable Monte Carlo Inference arXiv preprint arXiv:1901.00533 (2019) Maksym
Byshkin, Alex Stivala, Antonietta Mira, Garry Robins, Alessandro
Lomi, Fast Maximum Likelihood estimation via Equilibrium
Expectation for Large Network Data, Scientific Reports 8:11509 (2018) [preprint] Byshkin
M,
Stivala A, Mira A, Krause R, Robins G, Lomi A, Auxiliary
Parameter
MCMC for
Exponential
Random Graph Models, Journal of Statistical Physics 165:
740754 (2016) [preprint] Conference presentations Fast maximum likelihood estimation via equilibrium expectation for large network data, 13th International Conference in Monte Carlo & QuasiMonte Carlo Methods in Scientific Computing, July 16, 2018, Rennes, France Maximum Likelihood estimation via Equilibrium Expectation for large network data, Sunbelt INSNA Conference, June 26  July 1, 2018, Utrecht, Netherlands Fast maximum likelihood estimation via equilibrium expectation for large network data, Swiss Numeric Day, April 20th, 2018, Zürich, Switzerland Efficient Markov chain Monte Carlo Estimation of Exponential Random Graph Models, Second Australian Social Network Analysis Conference, November 28  29, 2017, Sydney, Australia Fast maximum likelihood estimation via MCMC equilibrium expectation for the statistical analysis of large networks, Cambridge Networks Day, 13th June 2017, Cambridge, UK Efficient MCMC Estimation for Exponential Random Graph Models, Sunbelt INSNA Conference, May 30  June 4, 2017, Beijing, China Efficient MCMC estimation of structural features of social and other networks, Complex Networks, March 20  24, 2017, Dubrovnik, Croatia
