The number of network science applications across many different fields has been rapidly
increasing. Surprisingly, the development of theory and domain-specific applications often …

Network models describe the brain as sets of nodes and edges that represent its distributed
organization. So far, most discoveries in network neuroscience have prioritized insights that …

Networks can describe the structure of a wide variety of complex systems by specifying
which pairs of entities in the system are connected. While such pairwise representations are …

Despite the development of large-scale data-acquisition techniques, experimental
observations of complex systems are often limited to a tiny fraction of the system under …

The Cambridge Manual to Archaeological Network Science provides the first
comprehensive guide to a field of research that has firmly established itself within …

Real-world network datasets are typically obtained in ways that fail to capture all edges. The
patterns of missing data are often non-uniform as they reflect biases and other shortcomings …

Despite their importance in many ecological processes, collecting data and information on
ecological interactions is an exceedingly challenging task. For this reason, large parts of the …

Social network analysis provides an important framework for studying the causes,
consequences, and structure of social ties. However, standard self-report measures—for …

Complex networks are typically not homogeneous, as they tend to display an array of
structures at different scales. A feature that has attracted a lot of research is their modular …

Complex networked systems often exhibit higher-order interactions, beyond dyadic
interactions, which can dramatically alter their observed behavior. Consequently …