In this paper, we study linear filters to process signals defined on simplicial complexes, ie,
signals defined on nodes, edges, triangles, etc. of a simplicial complex, thereby generalizing …

Graph signal processing (GSP) has seen rapid developments in recent years. Since its
introduction around ten years ago, we have seen numerous new ideas and practical …

This paper introduces topological Slepians, ie, a novel class of signals defined over
topological spaces (eg, simplicial complexes) that are maximally concentrated on the …

While frequency-domain algorithms have been demonstrated to be powerful for
conventional nonlinear signal processing, there is still not much progress in literature …

To deal with high-dimensional data, graph filters have shown their power in both graph
signal processing and data science. However, graph filters process signals exploiting only …

Graph-based learning and estimation are fundamental problems in various applications
involving power, social, and brain networks, to name a few. While learning pair-wise …

To perform any meaningful optimization task, distribution grid operators need to know the
topology of their grids. Although power grid topology identification and verification has been …

In many areas such as computational biology, finance or social sciences, knowledge of an
underlying graph explaining the interactions between agents is of paramount importance but …