160 research outputs found
Recent developments in exponential random graph (p*) models for social networks
This article reviews new specifications for exponential random graph models proposed by Snijders, Pattison, Robins & Handcock (2006) and demonstrates their improvement over homogeneous Markov random graph models in fitting empirical network data. Not only do the new specifications show improvements in goodness of fit for various data sets, they also help to avoid the problem of near-degeneracy that often afflicts the fitting of Markov random graph models in practice, particularly to network data exhibiting high levels of transitivity. The inclusion of a new higher order transitivity statistic allows estimation of parameters of exponential graph models for many (but not all) cases where it is impossible to estimate parameters of homogeneous Markov graph models. The new specifications were used to model a large number of classical smallscale network data sets and showed a dramatically better performance than Markov graph models. We also review three current programs for obtaining maximum likelihood estimates of model parameters and we compare these Monte Carlo maximum likelihood estimates with less accurate pseudo-likelihood estimates. Finally we discuss whether homogeneous Markov random graph models may be superseded by the new specifications, and how additional elaborations may further improve model performance
Quantifying Triadic Closure in Multi-Edge Social Networks
Multi-edge networks capture repeated interactions between individuals. In
social networks, such edges often form closed triangles, or triads. Standard
approaches to measure this triadic closure, however, fail for multi-edge
networks, because they do not consider that triads can be formed by edges of
different multiplicity. We propose a novel measure of triadic closure for
multi-edge networks of social interactions based on a shared partner statistic.
We demonstrate that our operalization is able to detect meaningful closure in
synthetic and empirical multi-edge networks, where common approaches fail. This
is a cornerstone in driving inferential network analyses from the analysis of
binary networks towards the analyses of multi-edge and weighted networks, which
offer a more realistic representation of social interactions and relations.Comment: 19 pages, 5 figures, 6 table
How to analyze dynamic network patterns of high performing teams
AbstractThe dynamic communication network within teams affects the performance of teams. But how can we analyze these emerging networks? We identified three research areas that have to be included for this purpose. First we summarize empirical studies concerning team networks and performance to point out the need of longitudinal investigations. Second we present the multi-level multi-theoretical model by Monge and Contractor (2003) which provides a theoretical framework to explain the evolution of communication networks within teams. Third a stochastic model is introduced that allows analyzing event based data, like e-mail streams, using exponential random graph models. We propose to include these three research areas that enable researchers and practitioners to analyze dynamic network patterns of virtual teams
L’intreccio tra attività scientifica e invenzione industriale a Trieste
2nonemixedDe Stefano D.; Zaccarin S.DE STEFANO, Domenico; Zaccarin, Susann
Bayesian Exponential Random Graph Models with Nodal Random Effects
We extend the well-known and widely used Exponential Random Graph Model
(ERGM) by including nodal random effects to compensate for heterogeneity in the
nodes of a network. The Bayesian framework for ERGMs proposed by Caimo and
Friel (2011) yields the basis of our modelling algorithm. A central question in
network models is the question of model selection and following the Bayesian
paradigm we focus on estimating Bayes factors. To do so we develop an
approximate but feasible calculation of the Bayes factor which allows one to
pursue model selection. Two data examples and a small simulation study
illustrate our mixed model approach and the corresponding model selection.Comment: 23 pages, 9 figures, 3 table
Identifying overlapping terrorist cells from the Noordin Top actor-event network
Actor-event data are common in sociological settings, whereby one registers
the pattern of attendance of a group of social actors to a number of events. We
focus on 79 members of the Noordin Top terrorist network, who were monitored
attending 45 events. The attendance or non-attendance of the terrorist to
events defines the social fabric, such as group coherence and social
communities. The aim of the analysis of such data is to learn about the
affiliation structure. Actor-event data is often transformed to actor-actor
data in order to be further analysed by network models, such as stochastic
block models. This transformation and such analyses lead to a natural loss of
information, particularly when one is interested in identifying, possibly
overlapping, subgroups or communities of actors on the basis of their
attendances to events. In this paper we propose an actor-event model for
overlapping communities of terrorists, which simplifies interpretation of the
network. We propose a mixture model with overlapping clusters for the analysis
of the binary actor-event network data, called {\tt manet}, and develop a
Bayesian procedure for inference. After a simulation study, we show how this
analysis of the terrorist network has clear interpretative advantages over the
more traditional approaches of affiliation network analysis.Comment: 24 pages, 5 figures; related R package (manet) available on CRA
Exponential Random Graph Modeling for Complex Brain Networks
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) has remained sparse. Descriptive models based on a specific feature
of the graph (clustering coefficient, degree distribution, etc.) have dominated
connectivity research in neuroscience. Corresponding generative models have
been developed to reproduce one of these features. However, the complexity
inherent in whole-brain network data necessitates the development and use of
tools that allow the systematic exploration of several features simultaneously
and how they interact to form the global network architecture. ERGMs provide a
statistically principled approach to the assessment of how a set of interacting
local brain network features gives rise to the global structure. We illustrate
the utility of ERGMs for modeling, analyzing, and simulating complex
whole-brain networks with network data from normal subjects. We also provide a
foundation for the selection of important local features through the
implementation and assessment of three selection approaches: a traditional
p-value based backward selection approach, an information criterion approach
(AIC), and a graphical goodness of fit (GOF) approach. The graphical GOF
approach serves as the best method given the scientific interest in being able
to capture and reproduce the structure of fitted brain networks
- …