NS-CUK Seminar: V.T.Hoang, Review on "Principal Neighbourhood Aggregation for Graph Nets", NeurIPS 2020

1. 2023. 04. 07
Van Thuy Hoang
Network Science Lab
Dept. of Artificial Intelligence
The Catholic University of Korea
E-mail: hoangvanthuy90@gmail.com

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 present a consistently well-performing and parameter efficient encode-process-decode
architecture
 Previous work on tasks taken from classical graph theory focuses on evaluating the
performance of GNN models on a single task such as shortest path
contributions

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 believe this multi-task approach ensures that the GNNs are able to understand multiple
properties simultaneously, which is fundamental for solving complex graph problems.
 efficiently sharing parameters between the tasks suggests a deeper understanding of the
structural features of the graphs.
 Explore the generalization ability of the networks by testing on graphs of larger sizes than
those present in the training set
Hypothesis

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Theorem

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 a single GNN layer and continuous input feature spaces, some aggregators fail to differentiate
between neighbourhood messages
test

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 XXXX
2.2 Degree-based scalers

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 XXXX
tensor product.

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Hidden layer

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Diagram for the Principal Neighbourhood Aggregation or PNA.

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Classification tasks

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Benchmarks
Log10 of the ratio of the MSE

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 XXXX
XXX

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