292 research outputs found
Estimating within-household contact networks from egocentric data
Acute respiratory diseases are transmitted over networks of social contacts.
Large-scale simulation models are used to predict epidemic dynamics and
evaluate the impact of various interventions, but the contact behavior in these
models is based on simplistic and strong assumptions which are not informed by
survey data. These assumptions are also used for estimating transmission
measures such as the basic reproductive number and secondary attack rates.
Development of methodology to infer contact networks from survey data could
improve these models and estimation methods. We contribute to this area by
developing a model of within-household social contacts and using it to analyze
the Belgian POLYMOD data set, which contains detailed diaries of social
contacts in a 24-hour period. We model dependency in contact behavior through a
latent variable indicating which household members are at home. We estimate
age-specific probabilities of being at home and age-specific probabilities of
contact conditional on two members being at home. Our results differ from the
standard random mixing assumption. In addition, we find that the probability
that all members contact each other on a given day is fairly low: 0.49 for
households with two 0--5 year olds and two 19--35 year olds, and 0.36 for
households with two 12--18 year olds and two 36+ year olds. We find higher
contact rates in households with 2--3 members, helping explain the higher
influenza secondary attack rates found in households of this size.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS474 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Estimating within-school contact networks to understand influenza transmission
Many epidemic models approximate social contact behavior by assuming random
mixing within mixing groups (e.g., homes, schools and workplaces). The effect
of more realistic social network structure on estimates of epidemic parameters
is an open area of exploration. We develop a detailed statistical model to
estimate the social contact network within a high school using friendship
network data and a survey of contact behavior. Our contact network model
includes classroom structure, longer durations of contacts to friends than
nonfriends and more frequent contacts with friends, based on reports in the
contact survey. We performed simulation studies to explore which network
structures are relevant to influenza transmission. These studies yield two key
findings. First, we found that the friendship network structure important to
the transmission process can be adequately represented by a dyad-independent
exponential random graph model (ERGM). This means that individual-level sampled
data is sufficient to characterize the entire friendship network. Second, we
found that contact behavior was adequately represented by a static rather than
dynamic contact network.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS505 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
A statnet Tutorial
The statnet suite of R packages contains a wide range of functionality for the statistical analysis of social networks, including the implementation of exponential-family random graph (ERG) models. In this paper we illustrate some of the functionality of statnet through a tutorial analysis of a friendship network of 1,461 adolescents.
ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks
We describe some of the capabilities of the ergm package and the statistical theory underlying it. This package contains tools for accomplishing three important, and inter-related, tasks involving exponential-family random graph models (ERGMs): estimation, simulation, and goodness of fit. More precisely, ergm has the capability of approximating a maximum likelihood estimator for an ERGM given a network data set; simulating new network data sets from a fitted ERGM using Markov chain Monte Carlo; and assessing how well a fitted ERGM does at capturing characteristics of a particular network data set.
statnet: Software Tools for the Representation, Visualization, Analysis and Simulation of Network Data
statnet is a suite of software packages for statistical network analysis. The packages implement recent advances in network modeling based on exponential-family random graph models (ERGM). The components of the package provide a comprehensive framework for ERGM-based network modeling, including tools for model estimation, model evaluation, model-based network simulation, and network visualization. This broad functionality is powered by a central Markov chain Monte Carlo (MCMC) algorithm. The coding is optimized for speed and robustness.
Characterizing Ranked Chinese Syllable-to-Character Mapping Spectrum: A Bridge Between the Spoken and Written Chinese Language
One important aspect of the relationship between spoken and written Chinese
is the ranked syllable-to-character mapping spectrum, which is the ranked list
of syllables by the number of characters that map to the syllable. Previously,
this spectrum is analyzed for more than 400 syllables without distinguishing
the four intonations. In the current study, the spectrum with 1280 toned
syllables is analyzed by logarithmic function, Beta rank function, and
piecewise logarithmic function. Out of the three fitting functions, the
two-piece logarithmic function fits the data the best, both by the smallest sum
of squared errors (SSE) and by the lowest Akaike information criterion (AIC)
value. The Beta rank function is the close second. By sampling from a Poisson
distribution whose parameter value is chosen from the observed data, we
empirically estimate the -value for testing the
two-piece-logarithmic-function being better than the Beta rank function
hypothesis, to be 0.16. For practical purposes, the piecewise logarithmic
function and the Beta rank function can be considered a tie.Comment: 15 pages, 4 figure
Tempus volat, hora fugit: A survey of tie‐oriented dynamic network models in discrete and continuous time
Given the growing number of available tools for modeling dynamic networks, the choice of a suitable model becomes central. The goal of this survey is to provide an overview of tie‐oriented dynamic network models. The survey is focused on introducing binary network models with their corresponding assumptions, advantages, and shortfalls. The models are divided according to generating processes, operating in discrete and continuous time. First, we introduce the temporal exponential random graph model (TERGM) and the separable TERGM (STERGM), both being time‐discrete models. These models are then contrasted with continuous process models, focusing on the relational event model (REM). We additionally show how the REM can handle time‐clustered observations, that is, continuous‐time data observed at discrete time points. Besides the discussion of theoretical properties and fitting procedures, we specifically focus on the application of the models on two networks that represent international arms transfers and email exchange, respectively. The data allow to demonstrate the applicability and interpretation of the network models
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
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