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..
How Powerful are Graph Neural Networks?
Authors: Keyulu Xu*, Weihua Hu*, Jure Leskovec, Stefanie Jegelka
ICLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically validate our theoretical findings on a number of graph classification benchmarks, and demonstrate that our model achieves state-of-the-art performance. |
| Researcher Affiliation | Academia | Keyulu Xu MIT EMAIL Weihua Hu Stanford University EMAIL Jure Leskovec Stanford University EMAIL Stefanie Jegelka MIT EMAIL |
| Pseudocode | No | The paper uses mathematical equations to describe the model updates (e.g., Eq 2.1, 4.1) but does not include explicit pseudocode blocks or algorithms. |
| Open Source Code | Yes | 1The code is available at https://github.com/weihua916/powerful-gnns. |
| Open Datasets | Yes | We use 9 graph classification benchmarks: 4 bioinformatics datasets (MUTAG, PTC, NCI1, PROTEINS) and 5 social network datasets (COLLAB, IMDB-BINARY, IMDB-MULTI, REDDITBINARY and REDDIT-MULTI5K) (Yanardag & Vishwanathan, 2015). |
| Dataset Splits | Yes | Following (Yanardag & Vishwanathan, 2015; Niepert et al., 2016), we perform 10-fold crossvalidation with LIB-SVM (Chang & Lin, 2011). |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper mentions software components like 'LIB-SVM', 'Adam optimizer', and 'Batch normalization' but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | For all configurations, 5 GNN layers (including the input layer) are applied, and all MLPs have 2 layers. Batch normalization (Ioffe & Szegedy, 2015) is applied on every hidden layer. We use the Adam optimizer (Kingma & Ba, 2015) with initial learning rate 0.01 and decay the learning rate by 0.5 every 50 epochs. The hyper-parameters we tune for each dataset are: (1) the number of hidden units {16, 32} for bioinformatics graphs and 64 for social graphs; (2) the batch size {32, 128}; (3) the dropout ratio {0, 0.5} after the dense layer (Srivastava et al., 2014); (4) the number of epochs, i.e., a single epoch with the best cross-validation accuracy averaged over the 10 folds was selected. |