1.22 MB, PDF document
Research output: Working paper › Preprint
Research output: Working paper › Preprint
}
TY - UNPB
T1 - Modelling Populations of Interaction Networks via Distance Metrics
AU - Bolt, George
AU - Lunagómez, Simón
AU - Nemeth, Christopher
N1 - 42 pages (76 with supplementary materials), 16 figures
PY - 2022/6/20
Y1 - 2022/6/20
N2 - Network data arises through observation of relational information between a collection of entities. Recent work in the literature has independently considered when (i) one observes a sample of networks, connectome data in neuroscience being a ubiquitous example, and (ii) the units of observation within a network are edges or paths, such as emails between people or a series of page visits to a website by a user, often referred to as interaction network data. The intersection of these two cases, however, is yet to be considered. In this paper, we propose a new Bayesian modelling framework to analyse such data. Given a practitioner-specified distance metric between observations, we define families of models through location and scale parameters, akin to a Gaussian distribution, with subsequent inference of model parameters providing reasoned statistical summaries for this non-standard data structure. To facilitate inference, we propose specialised Markov chain Monte Carlo (MCMC) schemes capable of sampling from doubly-intractable posterior distributions over discrete and multi-dimensional parameter spaces. Through simulation studies we confirm the efficacy of our methodology and inference scheme, whilst its application we illustrate via an example analysis of a location-based social network (LSBN) data set.
AB - Network data arises through observation of relational information between a collection of entities. Recent work in the literature has independently considered when (i) one observes a sample of networks, connectome data in neuroscience being a ubiquitous example, and (ii) the units of observation within a network are edges or paths, such as emails between people or a series of page visits to a website by a user, often referred to as interaction network data. The intersection of these two cases, however, is yet to be considered. In this paper, we propose a new Bayesian modelling framework to analyse such data. Given a practitioner-specified distance metric between observations, we define families of models through location and scale parameters, akin to a Gaussian distribution, with subsequent inference of model parameters providing reasoned statistical summaries for this non-standard data structure. To facilitate inference, we propose specialised Markov chain Monte Carlo (MCMC) schemes capable of sampling from doubly-intractable posterior distributions over discrete and multi-dimensional parameter spaces. Through simulation studies we confirm the efficacy of our methodology and inference scheme, whilst its application we illustrate via an example analysis of a location-based social network (LSBN) data set.
KW - stat.ME
KW - stat.CO
M3 - Preprint
BT - Modelling Populations of Interaction Networks via Distance Metrics
ER -