Papers & Talks
Data Sets & Examples
HNM: Hierarchical Network Models
project deals with the development, testing and deployment of models
for multiple social networks, particularly those with conditionally
independent ties. We develop methods for single networks, as
as for partial pooling across ensembles of networks, and explore both
the influence of covariates and interventions, and the evolution of
networks over time.
on multiple social networks arising from the same generative
mechanisms, and evolving over time together, are becoming increasingly
available in education research, public health and the social sciences.
Our work provides new, clearly formulated methodology and models for
this type of data, rather than treating each network
separately or assuming that all come from exactly the same model. We
are also developing R packages for fitting these models, that can be
used by any researcher with multiple network data.
work elaborates, and explores the operating characteristics of, the
Hierarchical Network Models (HNM) framework of Sweet, Thomas and Junker
(2013), including the Hierarchical Latent Space
Hierarchical Mixed-Membership Stochastic Block Model for ensembles of
networks. With this framework one can model
and detect the effects of interventions and other covariates on
structure of social networks, by pooling information across ensembles
networks (teachers’ professional networks across multiple school
students’ peer networks across multiple geographic areas, etc.).
CID: Conditionally Independent Dyads
A key computational tool is the
R package CIDnet (Thomas, Dabbs, Sweet,Saldinle & Junker,
which allows for mixures of network structure and pools information
across multiple networks through hierarchical Bayes structure;
covariates affecting some (e.g., an intervention) or all (e.g. a node
or tie covariate) networks can also be modelled. We also
study how model parameters evolve through time, through model-dependent
autoregression and other smoothing methods, and we illustrate our
results in analyses of simulated and
real-world data sets.
validation are also being developed, both for comparison to other
models and for directly assessing the adequacy of a model's fit to
data, e.g with cross-validation methods.
initial implementation of CIDnet and related software for the HLSM and
HMMBSM is based on standard
methods such as Markov Chain Monte Carlo. In anticipation of
wider deployment of these models, we are also exploring faster
inferential procedures based on
Variational Inference and Sequential Monte Carlo.
This work has been generously
supported by the following grants.
expressed in any materials on this website are those of the authors of
the materials, and are not intended to reflect the views of the funding
- Hierarchical Models for the
Formation and Evolution of Ensembles of Social Networks. National
Science Foundation, Measurement Methodology and Statistics Program.
- Hierarchical Network Models for
Education Research. Institute of Educational Sciences (US
Department of Education). Award #R305D120004.
- Program in Interdisciplinary
Education Research. Institute of Educational
Sciences (US Department of Education). Award #R305B040063.
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Department of Statistics, Carnegie Mellon University
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