University of Cambridge > Talks.cam > Artificial Intelligence Research Group Talks (Computer Laboratory) > Improving the Aggregation in Graph Networks: can nodes understand their neighbourhood?

Improving the Aggregation in Graph Networks: can nodes understand their neighbourhood?

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Graph Neural Networks have been shown to be effective models for different predictive tasks on graph-structured data. This talk will combine the studies on the Principal Neighbourhood Aggregation (NeurIPS 2020) and the Directional Graph Networks (oral at DiffGeo4DL workshop at NeurIPS 2020). We will examine the expressive power of graph neural networks showing the limitations when it comes to the continuous feature spaces and directional kernels. Each of these will motivate improvements to the aggregation method of GNNs which will lead us to fully generalize CNNs. Empirical results from molecular chemistry and computer vision benchmarks will validate our findings.

This talk is part of the Artificial Intelligence Research Group Talks (Computer Laboratory) series.

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