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 …

The Densest Subgraph Problem requires us to find, in a given graph, a subset of vertices
whose induced subgraph maximizes a measure of density. The problem has received a …

Hypergraph neural networks (HNNs) using neural networks to encode hypergraphs provide
a promising way to model higher-order relations in data and further solve relevant prediction …

Hypergraphs are a natural modeling paradigm for networked systems with multiway
interactions. A standard task in network analysis is the identification of closely related or …

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 …

Core-periphery detection is a key task in exploratory network analysis where one aims to
find a core, a set of nodes well connected internally and with the periphery, and a periphery …

Hypergraphs are a useful abstraction for modeling multiway relationships in data, and
hypergraph clustering is the task of detecting groups of closely related nodes in such data …

Higher-order network analysis uses the ideas of hypergraphs, simplicial complexes,
multilinear and tensor algebra, and more, to study complex systems. These are by now well …

Learning on graphs is a subject of great interest due to the abundance of relational data
from real-world systems. Many of these systems involve higher-order interactions (super …

Dynamical systems on hypergraphs can display a rich set of behaviors not observable for
systems with pairwise interactions. Given a distributed dynamical system with a putative …