With the impressive growth of available data and the flexibility of network modelling, the
problem of devising effective quantitative methods for the comparison of networks arises …

Statistical network modelling has focused on representing the graph as a discrete structure,
namely the adjacency matrix. When assuming exchangeability of this array—which can aid …

The development of models and methodology for the analysis of data from multiple
heterogeneous networks is of importance both in statistical network theory and across a …

Significant progress has been made recently on theoretical analysis of estimators for the
stochastic block model (SBM). In this paper, we consider the multi-graph SBM, which serves …

Advances in understanding the structural connectomes of human brain require improved
approaches for the construction, comparison and integration of high-dimensional whole …

Bayesian Inference and Testing of Group Differences in Brain Networks Page 1 Bayesian Analysis
(2018) 13, Number 1, pp. 29–58 Bayesian Inference and Testing of Group Differences in Brain …

Network datasets typically exhibit certain types of statistical patterns, such as within-dyad
correlation, degree heterogeneity, and triadic patterns such as transitivity and clustering. The …

We introduce a Bayesian approach to conduct inferential analyses on dyadic data while
accounting for interdependencies between observations through a set of additive and …

Advanced brain imaging techniques make it possible to measure individuals' structural
connectomes in large cohort studies non-invasively. Given the availability of large scale data …

In a globalized economy, production of goods can be disrupted by trade disputes. Yet the
resulting impacts on carbon dioxide emissions and ambient particulate matter (PM2. 5) …