NGG employs a variational graph autoencoder for graph compression and a diffusion process in the latent vector space, guided by vectors summarizing graph statistics. We demonstrate NGG's versatility ...

The success of graph neural networks (GNNs) largely relies on the process of aggregating information from neighbors defined by the input graph structures. Notably, message passing based GNNs, e.g., ...

In this work, we present Stochastic Graph Neural Diffusion, which approaches deep learning on graphs as a continuous stochastic heat diffusion process. We generalize the Stochastic Heat Equation on ...

Graph Convolutional Networks (GCNs) have recently received a lot of attention, owing to their ability to handle graph-structured data. To improve the expressive power of GCNs, several recent studies ...

InstructG2I was tested on three datasets from different domains – ART500K, Amazon, and Goodreads. For text-to-image methods, Stable Diffusion 1.5 was decided as the baseline model, and for ...