Thrilled to announce that our paper on the "Principal Neighbourhood Aggregation for Graph Nets" has been accepted at
@NeurIPSConf
#NeurIPS2020
! Great thanks to the amazing team of co-authors
@lukecavabarrett
,
@dom_beaini
,
@pl219_Cambridge
and
@PetarV_93
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Using Borsuk-Ulam theorem we show that multiple aggregation functions are necessary for nodes to distinguish their neighbourhoods in GNN with continuous features. (2/n)
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We also propose scalers as a way to give nodes information about their degree and so generalise the injectivity property of the sum aggregator (from
@KeyuluXu
&
@weihua916
et al.). (3/n)
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We then integrate these theoretical findings to propose an aggregation method, which we call Principal Neighbourhood Aggregation or PNA, combining multiple aggregators and logarithmic degree scalers. (4/n)
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Finally, we insert this aggregation method in an MPNN framework and show that it outperforms other methods both in a new synthetic multi-task benchmark and in real-world ones (from
@vijaypradwi
&
@chaitjo
et al.). (5/n)
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You can find the paper on arXiv (new version will be out soon) , the code on GitHub , a short presentation on SlidesLive and a talk by
@PetarV_93
on YouTube . (6/n)
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@GabriCorso
@NeurIPSConf
@lukecavabarrett
@dom_beaini
@pl219_Cambridge
@PetarV_93
Big congratulations! 👊
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