Rethinking pooling in graph neural networks

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Abstract

Graph pooling is a central component of a myriad of graph neural network (GNN) architectures. As an inheritance from traditional CNNs, most approaches formulate graph pooling as a cluster assignment problem, extending the idea of local patches in regular grids to graphs. Despite the wide adherence to this design choice, no work has rigorously evaluated its influence on the success of GNNs. In this paper, we build upon representative GNNs and introduce variants that challenge the need for locality-preserving representations, either using randomization or clustering on the complement graph. Strikingly, our experiments demonstrate that using these variants does not result in any decrease in performance. To understand this phenomenon, we study the interplay between convolutional layers and the subsequent pooling ones. We show that the convolutions play a leading role in the learned representations. In contrast to the common belief, local pooling is not responsible for the success of GNNs on relevant and widely-used benchmarks.
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020)
Publication statusPublished - 2020
MoE publication typeA4 Article in a conference publication
EventIEEE Conference on Neural Information Processing Systems; - Virtual, Vancouver, Canada
Duration: 6 Dec 202012 Dec 2020
Conference number: 34

Publication series

NameAdvances in neural information processing systems
PublisherMorgan Kaufmann Publishers
Volume33
ISSN (Print)1049-5258

Conference

ConferenceIEEE Conference on Neural Information Processing Systems;
Abbreviated titleNeurIPS
Country/TerritoryCanada
CityVancouver
Period06/12/202012/12/2020

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