Statistical network analysis with bergm
Recent advances in computational methods for intractable models have made network data
increasingly amenable to statistical analysis. Exponential random graph models (ERGMs) …
increasingly amenable to statistical analysis. Exponential random graph models (ERGMs) …
Bergm: Bayesian exponential random graphs in R
In this paper we describe the main featuress of the Bergm package for the open-source R
software which provides a comprehensive framework for Bayesian analysis for exponential …
software which provides a comprehensive framework for Bayesian analysis for exponential …
[HTML][HTML] Highly scalable maximum likelihood and conjugate Bayesian inference for ERGMs on graph sets with equivalent vertices
The exponential family random graph modeling (ERGM) framework provides a highly
flexible approach for the statistical analysis of networks (ie, graphs). As ERGMs with dyadic …
flexible approach for the statistical analysis of networks (ie, graphs). As ERGMs with dyadic …
Selection of exponential-family random graph models via Held-Out Predictive Evaluation (HOPE)
Statistical models for networks with complex dependencies pose particular challenges for
model selection and evaluation. In particular, many well-established statistical tools for …
model selection and evaluation. In particular, many well-established statistical tools for …
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 …
EGBTER: Capturing degree distribution, clustering coefficients, and community structure in a single random graph model
O El-Daghar, E Lundberg… - 2018 IEEE/ACM …, 2018 - ieeexplore.ieee.org
Random graph models are important constructs for data analytic applications as well as
pure mathematical developments, as they provide capabilities for network synthesis and …
pure mathematical developments, as they provide capabilities for network synthesis and …
[HTML][HTML] Power and Multicollinearity in Small Networks: A Discussion of “Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of …
GG Vega Yon - Journal of the American Statistical Association, 2023 - Taylor & Francis
The recent work by Krivitsky, Coletti, and Hens [KCH] provides an important new contribution
to the Exponential-Family Random Graph Models [ERGMs], a start-to-finish approach to …
to the Exponential-Family Random Graph Models [ERGMs], a start-to-finish approach to …
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 …
Missing data augmentation for Bayesian exponential random multi-graph models
In this paper we present an estimation algorithm for Bayesian exponential random multi-
graphs (BERmGMs) under missing network data. Social actors are often connected with …
graphs (BERmGMs) under missing network data. Social actors are often connected with …