Low-dimensional representation and clustering of network data are tasks of great interest
across various fields. Latent position models are routinely used for this purpose by assuming …

Interactions between actors are frequently represented using a network. The latent position
model is widely used for analysing network data, whereby each actor is positioned in a …

In this chapter, we present a review of latent position models for networks. We review the
recent literature in this area and illustrate the basic aspects and properties of this modeling …

Network data, commonly used throughout the physical, social, and biological sciences,
consists of nodes (individuals) and the edges (interactions) between them. One way to …

Edge sampling is an important topic in network analysis. It provides a natural way to reduce
network size while retaining desired features of the original network. Sampling methods that …

Social network data are relational data recorded among a group of actors, interacting in
different contexts. Often, the same set of actors can be characterised by multiple social …

A rich class of network models associate each node with a low-dimensional latent
coordinate that controls the propensity for connections to form. Models of this type are well …

The assumptions underlying the estimation of Gravity equations imply that a country's
behavior is independent of the actions of others. In practice, a few countries heavily …

Data organized in array structures arise in various domains. Each entry of the array serves
as a statistical unit, while the dimensions correspond to indexing attributes. The inherent …

This dissertation explores several statistical challenges involving inference problems where
the object of interest is a latent phenomenon or involves missing data. Effective modeling of …