Network-based modeling of complex systems and data using the language of graphs has
become an essential topic across a range of different disciplines. Arguably, this graph-based …

Single-cell RNA-seq enables the quantitative characterization of cell types based on global
transcriptome profiles. We present single-cell consensus clustering (SC3), a user-friendly …

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 that …

Using graphs to model pairwise relationships between entities is a ubiquitous framework for
studying complex systems and data. Simplicial complexes extend this dyadic model of …

In recent years, there has been a surge of interest in community detection algorithms for
complex networks. A variety of computational heuristics, some with a long history, have been …

Community detection, the decomposition of a graph into essential building blocks, has been
a core research topic in network science over the past years. Since a precise notion of what …

In this tutorial, we provide a didactic treatment of the emerging topic of signal processing on
higher-order networks. Drawing analogies from discrete and graph signal processing, we …

Synchronization over networks depends strongly on the structure of the coupling between
the oscillators. When the coupling presents certain regularities, the dynamics can be coarse-…

The cardiac vascular and perivascular niche are of major importance in homeostasis and
during disease, but we lack a complete understanding of its cellular heterogeneity and …

We investigate consensus dynamics on temporal hypergraphs that encode network systems
with time-dependent, multiway interactions. We compare these consensus processes with …