Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Rethinking pooling in graph neural networks
Authors: Diego Mesquita, Amauri Souza, Samuel Kaski
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper, we study the extent to which local pooling plays a role in GNNs. In particular, we choose representative models that are popular or claim to achieve state-of-the-art performances and simplify their pooling operators by eliminating any clustering-enforcing component. We either apply randomized cluster assignments or operate on complementary graphs. Surprisingly, the empirical results show that the non-local GNN variants exhibit comparable, if not superior, performance to the original methods in all experiments. [...] We use four graph-level prediction tasks as running examples: predicting the constrained solubility of molecules (ZINC, [20]), classifying chemical compounds regarding their activity against lung cancer (NCI1, [40]); categorizing ego-networks of actors w.r.t. the genre of the movies in which they collaborated (IMDB-B, [45]); and classifying handwritten digits (Superpixels MNIST, [1, 10]). |
| Researcher Affiliation | Academia | Diego Mesquita1 , Amauri H. Souza2 , Samuel Kaski1,3 1Aalto University 2Federal Institute of Ceará 3University of Manchester EMAIL, EMAIL |
| Pseudocode | No | The paper contains mathematical equations but no explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | All methods were implemented in Py Torch [12, 33] and our code is available at https://github.com/AaltoPML/Rethinking-pooling-in-GNNs. |
| Open Datasets | Yes | We use four graph-level prediction tasks as running examples: predicting the constrained solubility of molecules (ZINC, [20]), classifying chemical compounds regarding their activity against lung cancer (NCI1, [40]); categorizing ego-networks of actors w.r.t. the genre of the movies in which they collaborated (IMDB-B, [45]); and classifying handwritten digits (Superpixels MNIST, [1, 10]). |
| Dataset Splits | Yes | We split each dataset into train (80%), validation (10%) and test (10%) data. |
| Hardware Specification | No | The paper mentions 'computational resources provided by the Aalto Science-IT Project' but does not provide specific hardware details such as GPU or CPU models used for experiments. |
| Software Dependencies | No | The paper states 'All methods were implemented in Py Torch' but does not provide specific version numbers for PyTorch or any other software libraries or dependencies. |
| Experiment Setup | Yes | We train all models with Adam [22] and apply learning rate decay, ranging from initial 10 3 down to 10 5, with decay ratio of 0.5 and patience of 10 epochs. Also, we use early stopping based on the validation accuracy. |