DERGMs: Degeneracy-restricted exponential random graph models
V Karwa, S Petrović, D Bajić - arXiv preprint arXiv:1612.03054, 2016 - arxiv.org
Exponential random graph models, or ERGMs, are a flexible and general class of models for
modeling dependent data. While the early literature has shown them to be powerful in …
modeling dependent data. While the early literature has shown them to be powerful in …
DERGMs: degeneracy-restricted exponential family random graph models
V Karwa, S Petrović, D Bajić - Network Science, 2022 - cambridge.org
Exponential random graph models, or ERGMs, are a flexible and general class of models for
modeling dependent data. While the early literature has shown them to be powerful in …
modeling dependent data. While the early literature has shown them to be powerful in …
Exponential-Family Models of Random Graphs
Exponential-family Random Graph Models (ERGMs) constitute a large statistical framework
for modeling dense and sparse random graphs with short-or long-tailed degree distributions …
for modeling dense and sparse random graphs with short-or long-tailed degree distributions …
ergm. graphlets: a package for ERG modeling based on graphlet statistics
Exponential-family random graph models (ERGMs) are probabilistic network models that are
parametrized by sufficient statistics based on structural (ie, graph-theoretic) properties. The …
parametrized by sufficient statistics based on structural (ie, graph-theoretic) properties. The …
Practical network modeling via tapered exponential-family random graph models
B Blackburn, MS Handcock - Journal of Computational and …, 2023 - Taylor & Francis
Abstract Exponential-family Random Graph Models (ERGMs) have long been at the forefront
of the analysis of relational data. The exponential-family form allows complex network …
of the analysis of relational data. The exponential-family form allows complex network …
Stochastic weighted graphs: Flexible model specification and simulation
In most domains of network analysis researchers consider networks that arise in nature with
weighted edges. Such networks are routinely dichotomized in the interest of using available …
weighted edges. Such networks are routinely dichotomized in the interest of using available …
Estimating and understanding exponential random graph models
S Chatterjee, P Diaconis - The Annals of Statistics, 2013 - projecteuclid.org
We introduce a method for the theoretical analysis of exponential random graph models.
The method is based on a large-deviations approximation to the normalizing constant …
The method is based on a large-deviations approximation to the normalizing constant …
Estimation of exponential random graph models for large social networks via graph limits
Analyzing and modeling network data have become increasingly important in a wide range
of scientific fields. Among popular models, exponential random graph models (ERGM) have …
of scientific fields. Among popular models, exponential random graph models (ERGM) have …
Second-order Approximation of Exponential Random Graph Models
WY Ding, X Fang - arXiv preprint arXiv:2401.01467, 2024 - arxiv.org
Exponential random graph models (ERGMs) are flexible probability models allowing edge
dependency. However, it is known that, to a first-order approximation, many ERGMs behave …
dependency. However, it is known that, to a first-order approximation, many ERGMs behave …
Block-approximated exponential random graphs
An important challenge in the field of exponential random graphs (ERGs) is the fitting of non-
trivial ERGs on large graphs. By utilizing fast matrix block-approximation techniques, we …
trivial ERGs on large graphs. By utilizing fast matrix block-approximation techniques, we …