Topological deep learning (TDL) is a rapidly evolving field that uses topological features to
understand and design deep learning models. This paper posits that TDL may complement …

Hypergraphs, describing networks where interactions take place among any number of
units, are a natural tool to model many real-world social and biological systems. Here, we …

Topological deep learning (TDL) is a rapidly evolving field that uses topological features to
understand and design deep learning models. This paper posits that TDL is the new frontier …

The enduring legacy of Euclidean geometry underpins classical machine learning, which,
for decades, has been primarily developed for data lying in Euclidean space. Yet, modern …

Representing social systems as networks, starting from the interactions between individuals,
sheds light on the mechanisms governing their dynamics. However, networks encode only …

Going beyond networks, to include higher-order interactions of arbitrary sizes, is a major
step to better describe complex systems. In the resulting hypergraph representation, tools to …

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 …

Current epidemics in the biological and social domains are challenging the standard
assumptions of mathematical contagion models. Chief among them are the complex …

Abstract We introduce TopoX, a Python software suite that provides reliable and user-
friendly building blocks for computing and machine learning on topological domains that …

A key challenge of nonlinear dynamics and network science is to understand how higher-
order interactions influence collective dynamics. Although many studies have approached …