3 accepted papers
Graph neural networks (GNNs) with a rescale invariance, such as GATs, can be re-parameterized during optimization through dynamic rescaling of network parameters and gradients while keeping the loss invariant. In this work, we explore dynamic rescaling as a tool to influence GNN training dynamics in…
Graph Attention Networks (GATs) are designed to provide flexible neighborhood aggregation that assigns weights to neighbors according to their importance. In practice, however, GATs are often unable to switch off task-irrelevant neighborhood aggregation, as we show experimentally and analytically. T…
While the expressive power and computational capabilities of graph neural networks (GNNs) have been theoretically studied, their optimization and learning dynamics, in general, remain largely unexplored. Our study undertakes the Graph Attention Network (GAT), a popular GNN architecture in which a no…