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 …
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) …
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 …
Discussion of “A Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks”
NMD Niezink - Journal of the American Statistical Association, 2023 - Taylor & Francis
I congratulate the authors on their timely and insightful article. Since the advent of network
analysis, there has been the question of the meaning of sample size in a network setting …
analysis, there has been the question of the meaning of sample size in a network setting …
[PDF][PDF] Consistent M-estimation of curved exponentialfamily random graph models with local dependence and growing neighborhoods
M Schweinberger, J Stewart - 2016 - researchgate.net
In general, statistical inference for exponential-family random graph models of dependent
random graphs given a single observation of a random graph is problematic. We show that …
random graphs given a single observation of a random graph is problematic. We show that …
Model selection framework for graph-based data
RS Caceres, L Weiner, MC Schmidt, BA Miller… - arXiv preprint arXiv …, 2016 - arxiv.org
Graphs are powerful abstractions for capturing complex relationships in diverse application
settings. An active area of research focuses on theoretical models that define the generative …
settings. An active area of research focuses on theoretical models that define the generative …
How do some Bayesian Network machine learned graphs compare to causal knowledge?
The graph of a Bayesian Network (BN) can be machine learned, determined by causal
knowledge, or a combination of both. In disciplines like bioinformatics, applying BN structure …
knowledge, or a combination of both. In disciplines like bioinformatics, applying BN structure …
Improving ERGM starting values using simulated annealing
Much of the theory of estimation for exponential family models, which include exponential-
family random graph models (ERGMs) as a special case, is well-established and maximum …
family random graph models (ERGMs) as a special case, is well-established and maximum …
Kernel-based approximate Bayesian inference for exponential family random graph models
Bayesian inference for exponential family random graph models (ERGMs) is a doubly-
intractable problem because of the intractability of both the likelihood and posterior …
intractable problem because of the intractability of both the likelihood and posterior …