Vempalli Naga Sai Saketh

Interpretable and Parameter Efficient Graph Neural Additive Models with Random Fourier Features

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…