Title: Natural Graph Networks

Bio: Pim de Haan is a second year PhD student at the University of Amsterdam and a research associate at Qualcomm AI research. Under supervision of Max Welling, he works on building machine learning methods that take into account the geometry and symmetries of the domain, using the mathematics of groups, representations and categories. Prior to his PhD, Pim was a visiting researcher at UC Berkeley's Robotics and AI Lab and obtained master's degrees in artificial intelligence in Amsterdam and in theoretical physics at the University of Cambridge.

Abstract A key requirement for graph neural networks is that they must process a graph in a way that does not depend on how the graph is described. Traditionally this has been taken to mean that a graph network must be equivariant to node permutations. Here we show that instead of equivariance, the more general concept of naturality is sufficient for a graph network to be well-defined, opening up a larger class of graph networks. We define global and local natural graph networks, the latter of which are as scalable as conventional message passing graph neural networks while being more flexible. We give one practical instantiation of a natural network on graphs which uses an equivariant message network parameterization, yielding good performance on several benchmarks.

Paper: https://arxiv.org/abs/2007.08349

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Music Reference: 

Damiano Baldoni - Thinking of You (Intro) https://freemusicarchive.org/music/Damiano_Baldoni/Old_Beat/Thinking_of_you_1513
Damiano Baldoni - A Ghra (Outro) https://freemusicarchive.org/music/Damiano_Baldoni/Lost_Dinasty/A_GhrO