Sorry, you need to enable JavaScript to visit this website.

Graph attention neural network (GAT) stands as a fundamental model within graph neural networks, extensively employed across various applications. It assigns different weights to different nodes for feature aggregation by comparing the similarity of features between nodes. However, as the amount and density of graph data increases, GAT's computational demands rise steeply. In response, we present FastGAT, a simpler and more efficient graph attention neural network with global-aware adaptive computational node attention.

Graph neural networks (GNNs), consisting of a cascade of layers applying a graph convolution followed by a pointwise nonlinearity, have become a powerful architecture to process signals supported on graphs. Graph convolutions (and thus, GNNs), rely heavily on knowledge of the graph for operation. However, in many practical cases the graph shift operator (GSO) is not known and needs to be estimated, or might change from training time to testing time.