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..
Semi-Supervised Classification with Graph Convolutional Networks
Authors: Thomas N. Kipf, Max Welling
ICLR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on a number of datasets demonstrate that our model compares favorably both in classification accuracy and efficiency (measured in wall-clock time) against state-of-the-art methods for semi-supervised learning. |
| Researcher Affiliation | Academia | Thomas N. Kipf University of Amsterdam EMAIL Max Welling University of Amsterdam Canadian Institute for Advanced Research (CIFAR) EMAIL |
| Pseudocode | Yes | Algorithm 1: WL-1 algorithm (Weisfeiler & Lehmann, 1968) |
| Open Source Code | Yes | Code to reproduce our experiments is available at https://github.com/tkipf/gcn. |
| Open Datasets | Yes | We consider three citation network datasets: Citeseer, Cora and Pubmed (Sen et al., 2008)... NELL is a dataset extracted from the knowledge graph introduced in (Carlson et al., 2010). |
| Dataset Splits | Yes | We choose the same dataset splits as in Yang et al. (2016) with an additional validation set of 500 labeled examples for hyperparameter optimization... We train all models for a maximum of 200 epochs (training iterations) using Adam (Kingma & Ba, 2015) with a learning rate of 0.01 and early stopping with a window size of 10, i.e. we stop training if the validation loss does not decrease for 10 consecutive epochs. |
| Hardware Specification | Yes | Hardware used: 16-core Intel R Xeon R CPU E5-2640 v3 @ 2.60GHz, Ge Force R GTX TITAN X |
| Software Dependencies | No | In practice, we make use of Tensor Flow (Abadi et al., 2015) for an efficient GPU-based implementation2 of Eq. 9 using sparse-dense matrix multiplications. The paper mentions TensorFlow but does not specify a version number. |
| Experiment Setup | Yes | We train all models for a maximum of 200 epochs (training iterations) using Adam (Kingma & Ba, 2015) with a learning rate of 0.01 and early stopping with a window size of 10... We used the following sets of hyperparameters for Citeseer, Cora and Pubmed: 0.5 (dropout rate), 5 10 4 (L2 regularization) and 16 (number of hidden units); and for NELL: 0.1 (dropout rate), 1 10 5 (L2 regularization) and 64 (number of hidden units). |