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

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 …

Translational research requires data at multiple scales of biological organization.
Advancements in sequencing and multi-omics technologies have increased the availability …

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 …

Despite the recently exhibited importance of higher-order interactions for various processes,
few flexible (null) models are available. In particular, most studies on hypergraphs focus on …

Higher-order networks are widely used to describe complex systems in which interactions
can involve more than two entities at once. In this paper, we focus on inclusion within higher …

Many empirical networks incorporate higher order relations between elements and therefore
are naturally modelled as, possibly directed and/or weighted, hypergraphs, rather than …

Quality function deployment (QFD) has been a widely-acknowledged tool for translating
customer requirements into quality product characteristics based on which product …

Recent advances in network science have resulted in two distinct research directions aimed
at augmenting and enhancing representations for complex networks. The first direction, that …

In this work we study the topological properties of temporal hypergraphs. Hypergraphs
provide a higher dimensional generalization of a graph that is capable of capturing multi …