GNN-VPA: A Variance-Preserving Aggregation Strategy for Graph Neural Networks

Lisa Schneckenreiter, Richard Freinschlag, Florian Sestak, Johannes Brandstetter, Günter Klambauer, Andreas Mayr

Keywords: graph neural networks, variance preserving pooling, expressivity, signal propagation

Abstract: The successful graph neural networks (GNNs) and particularly message passing neural networks critically depend on the functions employed for message aggregation and graph-level readout. Using signal propagation theory, we propose a variance-preserving aggregation function, which maintains the expressivity of GNNs while improving learning dynamics. Our results could pave the way towards normalizer-free or self-normalizing GNNs.

Submission Number: 77