3 accepted papers
Continuous-time dynamic graphs (CTDGs) provide a richer framework to capture fine-grained temporal patterns in evolving relational data. Long-range information propagation is a key challenge while learning representations, wherein it is important to retain and update information over long temporal h…
Graph Neural Networks \texttt{(GNNs)} excel at jointly modeling node features and topology, yet their \emph{black-box} nature limits their adoption in real-world applications where interpretability is desired. Inspired by the success of interpretable Neural Additive Models \texttt{(NAM)} for tabular…
We consider recovery of signals over product cell structures from subsampled observations. In particular, we consider product cell complexes, which can be factorized as the Cartesian product of two simplicial complexes. We focus on recovering edge and node signals from subsampled signals on the fact…