… Though signaling pathways can be (and often are) modeled using graphs, we instead use
a generalization known as directed hypergraphs. We find directed hypergraphs to be a useful …

… erarchical hypergraph convolutional network based on the userand group-level hypergraphs
is … For (2), we design a double-scale node dropout strategy to create selfsupervision signals …

… 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 …

… in the hypergraph can be interpreted as a signaling event, … pointing into ErbB13 in the
hypergraph: the first hyperedge … Figure 2B shows the complete hypergraph representation of …

… filtering of hypergraph signals in the spectral domain, and then deploy Chebyshev
approximation to reduce the computational complexity. Spectral filtering of hypergraph signals. The …

… By leveraging the citation intent information included in a hypergraph, a retrieval model can
… matching signals. The proposed model consists of a heterogeneous hypergraph encoder …

… This section depicts our methodology for encoding both collaborative signals and
collaborative knowledge signals using two specially designed encoder networks: Local Self-aware …

… to directly optimize the informativeness of the graph signals. We introduce a hypergraph …
The hypergraph learning module utilizes a learnable hypergraph structure to model the latent …

… To resolve this problem, we propose Hypergraph Attention Networks (HANs), which define
… represent the information of the low-level signals in alignment. Moreover, HANs dramatically …