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Results for Exponential Random Graph Models

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15 papers · ranked by Valyu relevance

01
PPubMed28 Oct 2010
Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory Comparing Brain Networks
Bernadette C. M. van Wijk, Cornelis J. Stam, Andreas Daffertshofer, Olaf Sporns
'Olaf Sporns'] Graph theory is a valuable framework to study the organization of functional and anatomical connections in the brain. Its use for comparing network topologies, however, is not without difficulties. Graph measures may be influenced by the number of nodes (N) and the average degree (k) of the network. The…
97relevance
02
PPubMed25 May 2011
Exponential Random Graph Modeling for Complex Brain Networks ERG Modeling for Complex Brain Networks
Sean L. Simpson, Satoru Hayasaka, Paul J. Laurienti, Olaf Sporns
Exponential random graph models (ERGMs), also known as p* models, have been utilized extensively in the social science literature to study complex networks and how their global structure depends on underlying structural components. However, the literature on their use in biological networks (especially brain networks)…
97relevance
03
PPubMed10 Nov 2015
A Simulation Study Comparing Epidemic Dynamics on Exponential Random Graph and Edge-Triangle Configuration Type Contact Network Models Comparing Epidemics on ERGMs and Configuration Type Network Models
David A. Rolls, Peng Wang, Emma McBryde, Philippa Pattison + 2 more
We compare two broad types of empirically grounded random network models in terms of their abilities to capture both network features and simulated Susceptible-Infected-Recovered (SIR) epidemic dynamics. The types of network models are exponential random graph models (ERGMs) and extensions of the configuration model.…
97relevance
04
AarXiv2012
Bayesian model selection for exponential random graph models
Alberto Caimo, Nial Friel
Exponential random graph models are a class of widely used exponential family models for social networks. The topological structure of an observed network is modelled by the relative prevalence of a set of local sub-graph configurations termed network statistics. One of the key tasks in the application of these models…
96relevance
05
PPubMed13 Apr 2017
Using Exponential Random Graph Models to Analyze the Character of Peer Relationship Networks and Their Effects on the Subjective Well-being of Adolescents
Can Jiao, Ting Wang, Jianxin Liu, Huanjie Wu + 2 more
'Xiaozhe Peng'] The influences of peer relationships on adolescent subjective well-being were investigated within the framework of social network analysis, using exponential random graph models as a methodological tool. The participants in the study were 1,279 students (678 boys and 601 girls) from nine junior middle…
94relevance
06
PPubMed19 Aug 2019
A Framework for Reconstructing Archaeological Networks Using Exponential Random Graph Models
Viviana Amati, Angus Mol, Termeh Shafie, Corinne Hofman + 1 more
'Ulrik Brandes'] Reconstructing ties between archaeological contexts may contribute to explain and describe a variety of past social phenomena. Several models have been formulated to infer the structure of such archaeological networks. The applicability of these models in diverse archaeological contexts is limited by…
92relevance
07
AarXiv2022
Misspecification Tests on Models of Random Graphs
Denise Helena Silva Duarte, Rafael Honório Pereira Alves
A class of models that have been widely used are the exponential random graph (ERG) models, which form a comprehensive family of models that include independent and dyadic edge models, Markov random graphs, and many other graph distributions, in addition to allow the inclusion of covariates that can lead to a better…
90relevance
08
PPubMed19 Jun 2024
A Bayesian multilevel model for populations of networks using exponential-family random graphs
Brieuc Lehmann, Simon White
The collection of data on populations of networks is becoming increasingly common, where each data point can be seen as a realisation of a network-valued random variable. Moreover, each data point may be accompanied by some additional covariate information and one may be interested in assessing the effect of these…
89relevance
09
AarXiv2017
A Class of Temporal Hierarchical Exponential Random Graph Models for Longitudinal Network Data
Ming Cao
As a representation of relational data over time series, longitudinal networks provide opportunities to study link formation processes. However, networks at scale often exhibits community structure (i.e. clustering), which may confound local structural effects if it is not considered appropriately in statistical…
89relevance
10
AarXiv3 May 2012
A cluster expansion approach to exponential random graph models
Mei Yin
The exponential family of random graphs is among the most widelystudied network models. We show that any exponential random graph model may alternatively be viewed as a lattice gas model with a finite Banach space norm. The system may then be treated by cluster expansion methods from statistical mechanics. In…
88relevance
11
AarXiv2017
Bergm: Bayesian exponential random graph models in R
Alberto Caimo, Nial Friel
Networks are relational data that can be defined as a collection of nodes interacting with each other and connected in a pairwise fashion. From a statistical point of view, networks are relational data represented as mathematical graphs. A graph consists of a set of n nodes and a set of m edges which define some sort…
88relevance
12
PPubMed26 Aug 2022
Highly scalable maximum likelihood and conjugate Bayesian inference for ERGMs on graph sets with equivalent vertices Scalable maximum likelihood and conjugate Bayesian inference for ERGMs on graph sets with equivalent vertices
Fan Yin, Carter T. Butts, Fabrizio De Vico Fallani
The exponential family random graph modeling (ERGM) framework provides a highly flexible approach for the statistical analysis of networks (i.e., graphs). As ERGMs with dyadic dependence involve normalizing factors that are extremely costly to compute, practical strategies for ERGMs inference generally employ a variety…
88relevance
13
PPubMed6 May 2010
Modularity and anti-modularity in networks with arbitrary degree distribution
Arend Hintze, Christoph Adami
Background Much work in systems biology, but also in the analysis of social network and communication and transport infrastructure, involves an in-depth analysis of local and global properties of those networks, and how these properties relate to the function of the network within the integrated system. Most often…
82relevance
14
AarXiv2012
Spectral Estimation of Conditional Random Graph Models for Large-Scale Network Data
Antonino Freno, Mikaela Keller, Gemma C. Garriga, Marc Tommasi
Generative models for graphs have been typically committed to strong prior assumptions concerning the form of the modeled distributions. Moreover, the vast majority of currently available models are either only suitable for characterizing some particular network properties (such as degree distribution or clustering…
75relevance
15
AarXiv2017
Bayesian inference on random simple graphs with power law degree distributions
Juho Lee, Creighton Heaukulani, Zoubin Ghahramani, Lancelot F. James + 1 more
'Seungjin Choi'] We present a model for random simple graphs with power law (i.e., heavy-tailed) degree distributions. To attain this behavior, the edge probabilities in the graph are constructed from Bertoin–Fujita–Roynette–Yor (BFRY) random variables, which have been recently utilized in Bayesian statistics for the…
64relevance

Results for Exponential Random Graph Models

Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory Comparing Brain Networks

Exponential Random Graph Modeling for Complex Brain Networks ERG Modeling for Complex Brain Networks

A Simulation Study Comparing Epidemic Dynamics on Exponential Random Graph and Edge-Triangle Configuration Type Contact Network Models Comparing Epidemics on ERGMs and Configuration Type Network Models

Bayesian model selection for exponential random graph models

Using Exponential Random Graph Models to Analyze the Character of Peer Relationship Networks and Their Effects on the Subjective Well-being of Adolescents

A Framework for Reconstructing Archaeological Networks Using Exponential Random Graph Models

Misspecification Tests on Models of Random Graphs

A Bayesian multilevel model for populations of networks using exponential-family random graphs

A Class of Temporal Hierarchical Exponential Random Graph Models for Longitudinal Network Data

A cluster expansion approach to exponential random graph models

Bergm: Bayesian exponential random graph models in R

Highly scalable maximum likelihood and conjugate Bayesian inference for ERGMs on graph sets with equivalent vertices Scalable maximum likelihood and conjugate Bayesian inference for ERGMs on graph sets with equivalent vertices

Modularity and anti-modularity in networks with arbitrary degree distribution

Spectral Estimation of Conditional Random Graph Models for Large-Scale Network Data

Bayesian inference on random simple graphs with power law degree distributions

Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory Comparing Brain Networks

Exponential Random Graph Modeling for Complex Brain Networks ERG Modeling for Complex Brain Networks

A Simulation Study Comparing Epidemic Dynamics on Exponential Random Graph and Edge-Triangle Configuration Type Contact Network Models Comparing Epidemics on ERGMs and Configuration Type Network Models

Bayesian model selection for exponential random graph models

Using Exponential Random Graph Models to Analyze the Character of Peer Relationship Networks and Their Effects on the Subjective Well-being of Adolescents

A Framework for Reconstructing Archaeological Networks Using Exponential Random Graph Models

Misspecification Tests on Models of Random Graphs

A Bayesian multilevel model for populations of networks using exponential-family random graphs

A Class of Temporal Hierarchical Exponential Random Graph Models for Longitudinal Network Data

A cluster expansion approach to exponential random graph models

Bergm: Bayesian exponential random graph models in R

Highly scalable maximum likelihood and conjugate Bayesian inference for ERGMs on graph sets with equivalent vertices Scalable maximum likelihood and conjugate Bayesian inference for ERGMs on graph sets with equivalent vertices

Modularity and anti-modularity in networks with arbitrary degree distribution

Spectral Estimation of Conditional Random Graph Models for Large-Scale Network Data

Bayesian inference on random simple graphs with power law degree distributions