Nial Friel
Datos Biográficos
| ID | 77047 |
|---|---|
| NOMBRE | Nial Friel |
| NOMBRES | Nial |
| APELLIDO | Friel |
| FIRMA | FRIEL N |
| AFILIACIONES | University College Dublin |
| ORCID | 0000-0003-4778-0254 |
| VERIFICADO | Sí |
| TOTAL DE OBRAS | 12 |
| TOTAL DE CITAS | 16 |
| TOTAL COMO AUTOR | 11 |
| TOTAL COMO EDITOR | 1 |
| PRIMER AÑO DE PUBLICACIÓN | 2010 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 2026 |
| ÍNDICE H | 3 |
A zero-inflated Poisson latent position cluster model
The Latent Position Model (LPM) is a popular approach for the statistical analysis of network data. A central aspect of this model is that it assigns nodes to random positions in a latent space, such that the probability of an interaction between each pair of individuals or nodes is determined by their distance in this latent space. A key feature of this model is that it allows one to visualize nuanced structures via the latent space representati…
Bayesian testing of scientific expectations under exponential random graph models
The exponential random graph (ERGM) model is a commonly used statistical framework for studying the determinants of tie formations from social network data. To test scientific theories under ERGMs, statistical inferential techniques are generally used based on traditional significance testing using p-values. This methodology has certain limitations, however, such as its inconsistent behavior when the null hypothesis is true, its inability to quan…
Social Simulation for a Digital Society
Choosing the number of groups in a latent stochastic blockmodel for dynamic networks
Latent stochastic blockmodels are flexible statistical models that are widely used in social network analysis. In recent years, efforts have been made to extend these models to temporal dynamic networks, whereby the connections between nodes are observed at a number of different times. In this paper, we propose a new Bayesian framework to characterize the construction of connections. We rely on a Markovian property to describe the evolution of no…
Efficient Bayesian inference for exponential random graph models by correcting the pseudo-posterior distribution
Inferring structure in bipartite networks using the latent blockmodel and exact ICL
We consider the task of simultaneous clustering of the two node sets involved in a bipartite network. The approach we adopt is based on use of the exact integrated complete likelihood for the latent blockmodel. Using this allows one to infer the number of clusters as well as cluster memberships using a greedy search. This gives a model-based clustering of the node sets. Experiments on simulated bipartite network data show that the greedy search a…
Bayesian model selection for the latent position cluster model for social networks
The latent position cluster model is a popular model for the statistical analysis of network data. This model assumes that there is an underlying latent space in which the actors follow a finite mixture distribution. Moreover, actors which are close in this latent space are more likely to be tied by an edge. This is an appealing approach since it allows the model to cluster actors which consequently provides the practitioner with useful qualitati…
Bayesian exponential random graph models with nodal random effects
Properties of latent variable network models
We derive properties of latent variable models for networks, a broad class of models that includes the widely used latent position models. We characterize several features of interest, with particular focus on the degree distribution, clustering coefficient, average path length, and degree correlations. We introduce the Gaussian latent position model, and derive analytic expressions and asymptotic approximations for its network properties. We pay…
Actor-Based Models for Longitudinal Networks
Bayesian model selection for exponential random graph models
Bayesian inference for exponential random graph models
Properties of latent variable network models
We derive properties of latent variable models for networks, a broad class of models that includes the widely used latent position models. We characterize several features of interest, with particular focus on the degree distribution, clustering coefficient, average path length, and degree correlations. We introduce the Gaussian latent position model, and derive analytic expressions and asymptotic approximations for its network properties. We pay…
Bayesian model selection for the latent position cluster model for social networks
The latent position cluster model is a popular model for the statistical analysis of network data. This model assumes that there is an underlying latent space in which the actors follow a finite mixture distribution. Moreover, actors which are close in this latent space are more likely to be tied by an edge. This is an appealing approach since it allows the model to cluster actors which consequently provides the practitioner with useful qualitati…
Inferring structure in bipartite networks using the latent blockmodel and exact ICL
We consider the task of simultaneous clustering of the two node sets involved in a bipartite network. The approach we adopt is based on use of the exact integrated complete likelihood for the latent blockmodel. Using this allows one to infer the number of clusters as well as cluster memberships using a greedy search. This gives a model-based clustering of the node sets. Experiments on simulated bipartite network data show that the greedy search a…
Choosing the number of groups in a latent stochastic blockmodel for dynamic networks
Latent stochastic blockmodels are flexible statistical models that are widely used in social network analysis. In recent years, efforts have been made to extend these models to temporal dynamic networks, whereby the connections between nodes are observed at a number of different times. In this paper, we propose a new Bayesian framework to characterize the construction of connections. We rely on a Markovian property to describe the evolution of no…
Bayesian inference for exponential random graph models
Bayesian model selection for exponential random graph models
Actor-Based Models for Longitudinal Networks
Bayesian exponential random graph models with nodal random effects
Properties of latent variable network models
We derive properties of latent variable models for networks, a broad class of models that includes the widely used latent position models. We characterize several features of interest, with particular focus on the degree distribution, clustering coefficient, average path length, and degree correlations. We introduce the Gaussian latent position model, and derive analytic expressions and asymptotic approximations for its network properties. We pay…
Efficient Bayesian inference for exponential random graph models by correcting the pseudo-posterior distribution
Inferring structure in bipartite networks using the latent blockmodel and exact ICL
We consider the task of simultaneous clustering of the two node sets involved in a bipartite network. The approach we adopt is based on use of the exact integrated complete likelihood for the latent blockmodel. Using this allows one to infer the number of clusters as well as cluster memberships using a greedy search. This gives a model-based clustering of the node sets. Experiments on simulated bipartite network data show that the greedy search a…
Bayesian model selection for the latent position cluster model for social networks
The latent position cluster model is a popular model for the statistical analysis of network data. This model assumes that there is an underlying latent space in which the actors follow a finite mixture distribution. Moreover, actors which are close in this latent space are more likely to be tied by an edge. This is an appealing approach since it allows the model to cluster actors which consequently provides the practitioner with useful qualitati…
Choosing the number of groups in a latent stochastic blockmodel for dynamic networks
Latent stochastic blockmodels are flexible statistical models that are widely used in social network analysis. In recent years, efforts have been made to extend these models to temporal dynamic networks, whereby the connections between nodes are observed at a number of different times. In this paper, we propose a new Bayesian framework to characterize the construction of connections. We rely on a Markovian property to describe the evolution of no…
Social Simulation for a Digital Society
Bayesian testing of scientific expectations under exponential random graph models
The exponential random graph (ERGM) model is a commonly used statistical framework for studying the determinants of tie formations from social network data. To test scientific theories under ERGMs, statistical inferential techniques are generally used based on traditional significance testing using p-values. This methodology has certain limitations, however, such as its inconsistent behavior when the null hypothesis is true, its inability to quan…
A zero-inflated Poisson latent position cluster model
The Latent Position Model (LPM) is a popular approach for the statistical analysis of network data. A central aspect of this model is that it assigns nodes to random positions in a latent space, such that the probability of an interaction between each pair of individuals or nodes is determined by their distance in this latent space. A key feature of this model is that it allows one to visualize nuanced structures via the latent space representati…
Computer Science (11 obras) · Artificial Intelligence (9 obras) · Bayesian Methods and Mixture Models (8 obras) · Bayesian probability (8 obras) · Mathematics (8 obras) · Complex Network Analysis Techniques (7 obras) · Graph (7 obras) · Theoretical Computer Science (7 obras) · Machine learning (6 obras) · Bayesian inference (5 obras)