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..
What graph neural networks cannot learn: depth vs width
Authors: Andreas Loukas
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Section 5 presents some empirical evidence of the theory. This section aims to empirically test the connection between the capacity dw of a GNNmp, the number of nodes n of its input, and its ability to solve a given task. |
| Researcher Affiliation | Academia | Andreas Loukas Ecole Polytechnique Fédérale Lausanne EMAIL |
| Pseudocode | Yes | Computational model 1 Message passing graph neural network (GNNmp) |
| Open Source Code | No | The paper does not contain any explicit statement about open-sourcing the code or providing a link to a code repository. |
| Open Datasets | No | I generated five distributions over graphs with n (8, 16, 24, 32, 44) nodes and an average diameter of (4, 6, 8, 9, 11), respectively (this was achieved by setting p (6, 8, 10, 12, 14), see Figure 3a). For each such distribution, I generated a training and test set consisting respectively of 1000 and 200 examples. |
| Dataset Splits | No | For each such distribution, I generated a training and test set consisting respectively of 1000 and 200 examples. Both sets were exactly balanced, i.e., any example graph from the training and test set had exactly 50% chance of containing a 4-cycle. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU models, CPU types, or other computing resources used for experiments. |
| Software Dependencies | No | The GNNmp chosen was that proposed by Xu et al. (2018), with the addition of residual connections this network outperformed all others that I experimented with. The paper mentions using Adam optimizer and learning rate decay, but does not specify version numbers for any software or libraries. |
| Experiment Setup | Yes | To this end, I performed grid search over the hyperparameters w (2, 10, 20) and d (5, 10, 20, 15). To reduce the dependence on the initial conditions and training length, for each hyperparameter combination, I trained 4 networks independently (using Adam and learning rate decay) for 4000 epochs. |