… uses the hypergraph to model the multi-agent system, improving the accuracy and specificity
of communication among agents. Our approach brings the concept of hypergraph for the …

… Embedding-based coarsening attempts to merge together self-similar regions of the input
hypergraph with respect to the structural signals provided by node embeddings. In contrast, …

… introduce hypergraph learning into the critic network of MA-SAC to enable the spatio-temporal
interactions from multiple intersections in the road network. This method fuses hypergraph …

… hypergraph signal processing tools, such as hypergraph … , we innovatively use the hypergraph
spectrum and the spectral … and the information of BOLD signals on the network, in this …

… aims to coordinate traffic signals effectively across multiple … incorporates PPO and directed
hypergraph module to extract … the dynamical construction of hypergraph. The effectiveness …

… A recovery algorithm ψ corresponds to the decoder which wishes to infer the n information
bits from the received signals. One can then see that recovering communities in hypergraphs …

… an algorithm that learns a tensor-based hypergraph representation from a set of signals.
As input, we consider a set of hypergraph signals associated with each node in a network …

… At the computational limit of other approaches D = 25, Hypergraph-MT and our model
attain a similar score, signaling the importance of considering large interactions. Beyond this …

… In this study, we introduced a suite of null models for directed hypergraphs encompassing
hypergraphs with the same in-degree, out-degree, head-size, and tail-size distributions, as …

… by hypergraphs has the potential to address this need. This relies on the ability of hypergraphs …
Hypergraphs generalise graphs, which already offer an intuitive way to represent a wide …