Poster
in
Workshop: The Symbiosis of Deep Learning and Differential Equations II
PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equations
Moshe Eliasof · Eldad Haber · Eran Treister
Abstract:
Graph neural networks are have shown their efficacy in fields such as computer vision, computational biology and chemistry, where data are naturally explained by graphs. However, unlike convolutional neural networks, deep graph networks do not necessarily yield better performance than shallow networks. This behaviour usually stems from the over-smoothing phenomenon. In this work, we propose a family of architecturesto control this behaviour by design. Our networks are motivated by numerical methods for solving Partial Differential Equations (PDEs) on manifolds, and as such, their behaviour can be explained by similar analysis.
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