GNN-VPA: A Variance-Preserving Aggregation Strategy for Graph Neural Networks
Lisa Schneckenreiter
2024 poster
in
Affinity Event: Tiny Papers Poster Session 1
in
Affinity Event: Tiny Papers Poster Session 1
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.
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