Semi-Supervised Classification with Graph Convolutional Networks
Authors: Thomas N. Kipf, Max Welling
ICLR 2017 | Conference PDF | Archive PDF | Plain Text | 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 T.N.Kipf@uva.nl Max Welling University of Amsterdam Canadian Institute for Advanced Research (CIFAR) M.Welling@uva.nl |
| 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). |