Detecting a community in a network is a matter of discerning the distinct features and
connections of a group of members that are different from those in other communities. The …

Clustering algorithms are a class of unsupervised machine learning (ML) algorithms that
feature ubiquitously in modern data science, and play a key role in many learning-based …

Complex networks have been successfully used to describe the spread of diseases in
populations of interacting individuals. Conversely, pairwise interactions are often not …

Networks provide a powerful formalism for modeling complex systems by using a model of
pairwise interactions. But much of the structure within these systems involves interactions …

A deluge of new data on real-world networks suggests that interactions among system units
are not limited to pairs, but often involve a higher number of nodes. To properly encode …

Hypergraphs, encoding structured interactions among any number of system units, have
recently proven a successful tool to describe many real-world biological and social …

Hypergraphs provide an effective abstraction for modeling multi-way group interactions
among nodes, where each hyperedge can connect any number of nodes. Different from …

Written at an introductory level, and featuring engaging case examples, this book reviews
the theory and practice of personal and egocentric network research. This approach offers …

Historically, infectious diseases caused considerable damage to human societies, and they
continue to do so today. To help reduce their impact, mathematical models of disease …

Non-pharmaceutical interventions are crucial to mitigate the COVID-19 pandemic and
contain re-emergence phenomena. Targeted measures such as case isolation and contact …