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 …

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 …

Many dynamical effects in biology, social and technological complex systems have recently
revealed their relevance to group interactions beyond traditional dyadic relationships …

Hypergraphs and simplical complexes both capture the higher-order interactions of complex
systems, ranging from higher-order collaboration networks to brain networks. One open …

Filters are fundamental in extracting information from data. For time series and image data
that reside on Euclidean domains, filters are the crux of many signal processing and …

Signal processing (SP) excels at analyzing, processing, and inferring information defined
over regular (first continuous, later discrete) domains such as time or space. Indeed, the last …

The natural world is full of complex systems characterized by intricate relations between
their components: from social interactions between individuals in a social network to …

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 …

Topological signals, ie, dynamical variables defined on nodes, links, triangles, etc. of higher-
order networks, are attracting increasing attention. However, the investigation of their …

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 …