Recent decades have seen a rise in the use of physics methods to study different societal
phenomena. This development has been due to physicists venturing outside of their …

Several research studies have shown that complex networks modeling real-world
phenomena are characterized by striking properties:(i) they are organized according to …

Cluster analysis finds groups in data automatically. Most methods have been heuristic and
leave open such central questions as: how many clusters are there? Which method should I …

Machine learning is a field at the intersection of statistics and computer science that uses
algorithms to extract information and knowledge from data. Its applications increasingly find …

We investigate the degree to which impact in science and technology is associated with
surprising breakthroughs, and how those breakthroughs arise. Identifying breakthroughs …

There have been rapid developments in model-based clustering of graphs, also known as
block modelling, over the last ten years or so. We review different approaches and …

Graph theory provides a language for studying the structure of relations, and it is often used
to study interactions over time too. However, it poorly captures the intrinsically temporal and …

This chapter describes the basic variants of the stochastic blockmodel (SBM), and a
consistent Bayesian formulation that allows readers to infer them from data. The focus is on …

Hypergraphs, describing networks where interactions take place among any number of
units, are a natural tool to model many real-world social and biological systems. Here, we …

Social–ecological networks (SENs) represent the complex relationships between ecological
and social systems and are a useful tool for analyzing and managing ecosystem services …