814 citations · 1.3k across the 40 of their papers we have counts for
43 papers
Cup and Cap Topological Neural Network
Marta Niedostatek, Ferran Hernandez Caralt, Runyue Wang +4
Topological Deep Learning (TDL) is designed to learn features associated with higher-dimensional simplices including not only nodes but also edges, triangles and so on. However, mo…
-core percolation on hypergraphs with anchor nodes
Hoseung Jang, Byungjoon Min, Ginestra Bianconi
Hypergraphs describe higher-order interactions that involve more than a pair of nodes. A characteristic feature of hypergraphs is that their robustness can be strongly affected by…
Directed extended-range percolation
Wenbo Liu, Yiwen Zeng, Xueming Liu +1
While for standard percolation directionality is known to increase the combinatorial complexity of percolation, here we show that when connectivity is ensured by paths of length $R…
Feedback percolation on complex networks
Hoseung Jang, Ginestra Bianconi, Byungjoon Min
Traditional percolation theory assumes static microscopic rules, limiting its ability to describe real-world complex systems where macroscopic order actively regulates local intera…
Topology and higher-order global synchronization on directed and hollow simplicial and cell complexes
Runyue Wang, Timoteo Carletti, Ginestra Bianconi
Higher-order networks encode the many-body interactions of complex systems ranging from the brain to biological transportation networks. Simplicial and cell complexes are ideal hig…
Learning Dirac Spectral Transforms for Topological Signals
Leonardo Di Nino, Tiziana Cattai, Sergio Barbarossa +2
The Dirac operator provides a unified framework for processing signals defined over different order topological domains, such as node and edge signals. Its eigenmodes define a spec…