Many complex systems involve interactions between more than two agents. Hypergraphs
capture these higher-order interactions through hyperedges that may link more than two …

Learning and processing of signals over hypergraph models have gained substantial
traction owing to the ability of hypergraphs in characterizing multilateral interactions. In this …

Graphs have been utilized as a powerful tool to model pairwise relationships between
people or objects. Such structure is a special type of a broader concept referred to as …

We propose high-order hypergraph walks as a framework to generalize graph-based
network science techniques to hypergraphs. Edge incidence in hypergraphs is quantitative …

Pyvis is a Python module that enables visualizing and interactively manipulating network
graphs in the Jupyter notebook, or as a standalone web application. Pyvis is built on top of …

Hypergraphs are important for processing data with higher-order relationships involving
more than two entities. In scenarios where explicit hypergraphs are not readily available, it is …

Higher-order interactions (HOIs) are ubiquitous in real-world complex systems and
applications, and thus investigation of deep learning for HOIs has become a valuable …

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 are natural generalization of graphs in which a single (hyper) edge can
connect any number of vertices. As a result, hypergraphs are suitable and useful to model …

Systems biologists use interaction graphs to model the behavior of biological systems at the
molecular level. In an iterative process, such biologists observe the reactions of living cells …