The complexity of many biological, social and technological systems stems from the richness
of the interactions among their units. Over the past decades, a variety of complex systems …

Hypergraphs have attracted increasing attention in recent years thanks to their flexibility in
naturally modeling a broad range of systems where high-order relationships exist among …

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 …

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 …

Background Representing biological networks as graphs is a powerful approach to reveal
underlying patterns, signatures, and critical components from high-throughput biomolecular …

From social to biological systems, many real-world systems are characterized by higher-
order, non-dyadic interactions. Such systems are conveniently described by hypergraphs …

Chromatin architecture, a key regulator of gene expression, can be inferred using chromatin
contact data from chromosome conformation capture, or Hi-C. However, classical Hi-C does …

Network data has become widespread, larger, and more complex over the years. Traditional
network data is dyadic, capturing the relations among pairs of entities. With the need to …

Hypergraphs that can depict interactions beyond pairwise edges have emerged as an
appropriate representation for modeling polyadic relations in complex systems. With the …

A method for hypergraph-based analysis and the design of manufacturing systems has been
developed. The reason for its development is the need to integrate the human workforce into …