Timezone: »

Rethinking pooling in graph neural networks
Diego Mesquita · Amauri Souza · Samuel Kaski

Thu Dec 10 09:00 AM -- 11:00 AM (PST) @ Poster Session 5 #1714

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.

Author Information

Diego Mesquita (Aalto University)
Amauri Souza (Federal Institute of Ceara)
Samuel Kaski (Aalto University and University of Manchester)

More from the Same Authors

  • 2021 Poster: De-randomizing MCMC dynamics with the diffusion Stein operator »
    Zheyang Shen · Markus Heinonen · Samuel Kaski
  • 2019 : Coffee/Poster session 1 »
    Shiro Takagi · Khurram Javed · Johanna Sommer · Amr Sharaf · Pierluca D'Oro · Ying Wei · Sivan Doveh · Colin White · Santiago Gonzalez · Cuong Nguyen · Mao Li · Tianhe Yu · Tiago Ramalho · Masahiro Nomura · Ahsan Alvi · Jean-Francois Ton · W. Ronny Huang · Jessica Lee · Sebastian Flennerhag · Michael Zhang · Abram Friesen · Paul Blomstedt · Alina Dubatovka · Sergey Bartunov · Subin Yi · Iaroslav Shcherbatyi · Christian Simon · Zeyuan Shang · David MacLeod · Lu Liu · Liam Fowl · Diego Mesquita · Deirdre Quillen
  • 2019 Poster: Machine Teaching of Active Sequential Learners »
    Tomi Peltola · Mustafa Mert Çelikok · Pedram Daee · Samuel Kaski
  • 2017 Poster: Non-Stationary Spectral Kernels »
    Sami Remes · Markus Heinonen · Samuel Kaski
  • 2017 Poster: Differentially private Bayesian learning on distributed data »
    Mikko Heikkilä · Eemil Lagerspetz · Samuel Kaski · Kana Shimizu · Sasu Tarkoma · Antti Honkela
  • 2014 Workshop: Machine Learning in Computational Biology »
    Oliver Stegle · Sara Mostafavi · Anna Goldenberg · Su-In Lee · Michael Leung · Anshul Kundaje · Mark B Gerstein · Martin Renqiang Min · Hannes Bretschneider · Francesco Paolo Casale · Loïc Schwaller · Amit G Deshwar · Benjamin A Logsdon · Yuanyang Zhang · Ali Punjani · Derek C Aguiar · Samuel Kaski